Smart-learning and learning path

ABSTRACT

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. The SLKRS ingests data in multiple formats from multiple sources, merges the data into a knowledge base based on computed strengths of terms in the sources, and assimilates the merged data to generate experiences. The SLKRS receives feedback from the knowledge seeker and computes a score based on the feedback and the query to artificially learn unique characteristics of the knowledge seeker. The SLKRS generates a learning path for the knowledge seeker on a graphical output, wherein the learning path&#39;s state transition points lead to a projected learning path determined by the knowledge seekers performance over one or more of subtopics, topics, and lessons.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 16/795,618 titled “SMART-LEARNING AND KNOWLEDGE RETRIEVALSYSTEM” filed Feb. 20, 2020, the contents of which are herebyincorporated herein by reference in their entirety.

BACKGROUND

The current state of e-learning does not make it a powerful contender totraditional face-to-face learning. Learning management systems generallydo not provide effective automated personalized feedback to learners.Learning management systems also do not provide learning experiencesthat draw on multiple aspects of learning including association ofrelated concepts in the increasingly multi-disciplinary era ofknowledge. Currently, e-learning might also lead to social isolation andstunted development of communication skills in academic settings. Atpresent, this is only remedied by blended learning environmentscomprising interaction with actual persons either in-person or throughvideo and audio conferencing. To address these lacunae, there is a needfor a personalized immersive e-learning experience involving anartificially intelligent understanding of a learner's learning needs andacademic shortcomings. Furthermore, there is a need for acomputer-implemented method and system that receives learners' feedbackthrough various modes—text, audio, and video—and generates appropriateresponses and suitably tailored learning experiences using artificialintelligence and machine learning.

For knowledge retrieval, existing systems typically crawl and collectinformation, search through the collected information using variousalgorithms, for example, the PageRank® algorithm from Google® when aquery arrives, and send a response to queries, and send information to abrowser or via an application programming interface (API) to therequesting system or person. They follow theCrawl/Collect-Search-Respond (CSR) process. The existing systems alsoeither serve up text, for example, Google® Search, images, for example,Google® Image Search, or videos, for example Google® YouTube. Thesedigital assets, namely, text, images, video, etc., are also typicallystored in separate data stores and users have to access these storesseparately. There is a need for a centralized repository of relevantinformation that can be queried to get an immediate relevant responsewith multiple digital assets related to the query. Digital assetmanagement that, apart from just storing, also tags and cross-referencesdigital assets for quick retrieval are available. However, there is aneed for a system that utilizes artificial intelligence and machinelearning to provide relevant and useful responses comprising multipledigital assets for queries in the education domain. There is a need foran intelligent system for knowledge representation and retrieval.

Jobs are increasingly in need of multi-disciplinary skills that can begained by combining multiple subjects and applying concepts from onesubject to another. A growing number of knowledge seekers are concernedthat they are not being taught what they need to learn. They arelearning the same things that people learned decades ago. Moreover,different people are made to learn the same things the same way.Furthermore, during the course of education, students are required tolearn a vast number of concepts each year, so reinforcing the studentswith those concepts and seeing what the student picks up does not work.Furthermore, e-learning is not interesting to knowledge seekers whenthey have to mostly sit and watch videos. They need a better way tolearn. There is a need for a system that adapts to individual knowledgeseekers to teach them an integrated multidisciplinary curriculum throughan immersive, fun, and intimate learning experience. Moreover, there isa need for personalization of the learning process for each knowledgeseeker. Furthermore, there is a need to capture a knowledge seeker'scurrent knowledge level and capability to allow the knowledge seeker toapply and understand concepts, and use the information to allow theknowledge seeker get proficient in concepts that they find difficult tocomprehend through interactive and interesting means. Furthermore, thereis a need for a system that is always accessible through e-learning thatadapts and engages a knowledge seeker to make him or her proficient athis or her own pace while maintaining interest levels.

Furthermore, there is a need for a method and system that guides theknowledge seeker through a specific learning journey that maximizes theknowledge seeker's learning potential.

Hence, there is a long felt but unresolved need for acomputer-implemented method and a system that provides adaptive andpersonalized e-learning based on the continually, artificially learned,and unique characteristics of a knowledge seeker.

SUMMARY OF THE INVENTION

This summary is provided to introduce a selection of concepts in asimplified form that are further disclosed in the detailed descriptionof the invention. This summary is not intended to determine the scope ofthe claimed subject matter.

The computer-implemented method and smart-learning and knowledgeretrieval system (SLKRS) disclosed herein address the above recited needfor providing adaptive and personalized e-learning based on continuallyartificially learned unique characteristics of a knowledge seeker. Thecomputer-implemented method, using the SLKRS, provides artificialintelligence based knowledge representation and retrieval to answerqueries from a knowledge seeker or another system. The method adaptsmultidisciplinary educational courses to knowledge seekers and presentan immersive, fun and intimate e-learning experience. For example, themethod teaches applied computer science through an integrated,interdisciplinary curriculum designed to engage each knowledge seeker'sunique interests. The method and the SLKRS utilize artificialintelligence and machine learning to adapt and personalize the learningprocesses to each knowledge seeker. The method, using the SLKRS,generates personalized knowledge concept graphs for each knowledgeseeker. Through assessments and interactions, the method captures what aknowledge seeker knows and how well he or she can apply and understandconcepts. The method, using the SLKRS, assists the knowledge seekerlearn concepts he or she struggles with by connecting those concepts tosubjects where he or she thrives all while using interactive interfacesto provide constant and immediate help in a fun and interactive manner.The method uses regular assessments to gauge progress and continuouslyadapts learning paths to make every knowledge seeker proficient at hisor her own pace. The method provides a learning platform that is alwaysaccessible to knowledge seekers through an Internet connection.

The computer-implemented method and the smart-learning and knowledgeretrieval system (SLKRS) provides adaptive and personalized e-learningbased on continually artificially learned unique characteristics of aknowledge seeker. The SLKRS ingests data from multiple sources inmultiple formats including the Internet, documents in a plurality offormats, social media streams, blogs, web pages, videos, audios, images,games, virtual reality simulations, and augmented reality simulations.The SLKRS merges the data into a knowledge base to create an ontologybased on computed strengths of terms found in the sources from which thedata is ingested. The SLKRS computes the strengths of terms found in thesources using parameters comprising frequency of occurrence of thoseterms in other sources, relevance of the terms to an already createdportion of the ontology, and inputs from the knowledge seeker. The SLKRSindexes and tags the sources from which the data is ingested based onthe computed strengths of the terms found in those sources. The SLKRSassimilates the merged data to generate experiences. The SLKRS uses theassimilation of the merged data to create knowledge concept graphs basedon the computed strengths of the terms found in the sources from whichthe data is ingested. The SLKRS builds experiences from digital assets,for example, videos, games, virtual reality simulations, and augmentedreality simulations, in the merged data. Digital assets are text ormedia that are in a machine-readable format and which include rights touse them. Experiences are digital assets that are generated by theSLKRS.

The smart-learning and knowledge retrieval system (SLKRS) receives aquery from the knowledge seeker through one of multiple interfacescomprising text boxes, chat interfaces, voice interfaces, phoneapplications, interactive chat bots, virtual reality interfaces andaugmented reality interfaces. The SLKRS understands the received queryusing artificial intelligence and machine learning programs executableby at least one processor configured to execute computer programinstructions. The SLKRS retrieves and sends one of the generatedexperiences or an experience created based on the artificiallyintelligent understanding of the received query in an immersive formatto the knowledge seeker through one of the interfaces to interact withthe sent experience. The SLKRS receives feedback from the knowledgeseeker through one of the interfaces in response to the sent experience.The SLKRS continually computes a score based on queries and feedbackfrom the knowledge seeker to artificially learn unique characteristicsof the knowledge seeker. The SLKRS uses the computed score to measure anability of the knowledge seeker to learn and to show continued interestin an e-learning course. The SLKRS generates interventions and improvedexperiences for the knowledge seeker based on the computed score toprovide adaptive and personalized e-learning.

The SLKRS generates a learning path for the knowledge seeker as agraphical output on a user interface. The learning path's statetransition points lead to a projected learning path determined by theknowledge seeker's observed performance over one or more of sub topics,topics and lessons.

A learning path algorithm sets an initial learning level of the studentas average or “average expected level” in the learning path. Thelearning path algorithm will push specific content to the knowledgeseeker based on the type of learner the system perceives the knowledgeseeker to be, such as a fast learner, slow learner, depending on thelearning style of student, defined by the amount of time spent by thestudent on a topic, rate of understanding, frequency of visiting a topicof study, and performance in assessments. Past experiences on a subjectmatter can also be taken into account by another embodiment of thealgorithm, and the initial learning level of a student can be defined aswell. These parameters are exemplary, and additional parameters can bedetermined at a later time and added to the learning path algorithm forconsideration. The learning path algorithm can also be made to consideronly a subset of the parameters.

The learning path algorithm observes and computes learning proficiencyof the knowledge seeker along the learning path, and computes generalobservation points and state transition points. At the state transitionpoint, the learning path algorithm determines the future direction ofthe learning path, setting the knowledge seeker on the expected path ordeviating the path positively up or negatively down. Each lesson istaken as a point of transition to take the student on a path upwardswith improving performance or downwards with deteriorated performance.In another embodiment, each topic within the lesson is considered as apoint of transition to take the student on a path upwards with improvingperformance or downwards with deteriorated performance. The statetransition point leading to a projected learning path is determinedbased either on the performance of the knowledge seeker in the currentlesson or on the performance of the knowledge seeker in the currenttopic. In another embodiment, the state transition point leading to aprojected learning path is determined based on the aggregated computedperformance of the knowledge seeker in all previous lessons and topicsleading to the current lesson or topic. The learning path provide aflexible learning option for the knowledge seeker wherein the knowledgeseeker can slow down or hasten the learning pace. The observation pointsand state transitions points are flexible and can be determined by theuser of the learning path algorithm based on the needs of the user or anexternal system that is using the learning path algorithm.

The learning path algorithm can take inputs from a retention algorithm,and/or from a knowledge concept graph. The learning path algorithm canalso take inputs from external systems or other algorithms that need toutilize the functionality of the learning path algorithm.

One embodiment of the learning path algorithm is a modified HiddenMarkov model based algorithm. The modified Hidden Markov model basedalgorithm is exemplary.

Regular assessments of knowledge acquired by the knowledge seeker anddirect feedback from the knowledge seeker are used as inputs to derivethe transition points of the learning path.

In one or more embodiments, related systems comprise circuitry and/orprogramming for effecting the methods disclosed herein. The circuitryand/or programming can be any combination of hardware, software, and/orfirmware configured to effect the methods disclosed herein dependingupon the design choices of a system designer. Also, in an embodiment,various structural elements can be employed depending on the designchoices of the system designer.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofthe invention, is better understood when read in conjunction with theappended drawings. For illustrating the invention, exemplaryconstructions of the invention are shown in the drawings. However, theinvention is not limited to the specific methods andstructures/components disclosed herein. The description of a method stepor a structure/component referenced by a numeral in a drawing isapplicable to the description of that method step or structure/componentshown by that same numeral in any subsequent drawing herein.

FIG. 1 illustrates a computer-implemented method for providing adaptiveand personalized e-learning based on continually, artificially learnedunique characteristics of a knowledge seeker.

FIG. 2 exemplarily illustrates a system comprising the smart-learningand knowledge retrieval system for providing adaptive and personalizede-learning based on continually, artificially learned uniquecharacteristics of a knowledge seeker.

FIG. 3 exemplarily illustrates a microservice architecture that thesmart-learning and knowledge retrieval system utilizes for providingmicroservices to knowledge seekers.

FIG. 4 exemplarily illustrates a block diagram of an artificialintelligence microservices platform of the smart-learning and knowledgeretrieval system.

FIG. 5 exemplarily illustrates the internals of the microservicesplatform of the smart-learning and knowledge retrieval system.

FIG. 6 exemplarily illustrates an example of the microservices that thesmart-learning and knowledge retrieval system provides to knowledgeseekers.

FIG. 7 exemplarily illustrates a chatbot platform as an example of aninterface that the smart-learning and knowledge retrieval systemprovides to knowledge seekers for their interaction with thesmart-learning and knowledge retrieval system.

FIG. 8 exemplarily illustrates a software model for the smart-learningand knowledge retrieval system.

FIG. 9 is a block diagram representation of core strategy and drivers orfunctional enablers to ensure optimal user experience for a knowledgeseeker seeking knowledge on the smart-learning and knowledge retrievalsystem.

FIG. 10 exemplarily illustrates an architecture for the deployment,scaling, and management of the smart-learning and knowledge retrievalsystem.

FIG. 11 exemplarily illustrates an interface that the smart-learning andknowledge retrieval system provides to knowledge seekers for accessingsome of its microservices.

FIG. 12 illustrates a graphical user interface of an exemplaryembodiment of the smart-learning and knowledge retrieval systemdepicting an e-learning course.

FIG. 13 illustrates a graphical user interface of an exemplaryembodiment of a learning path.

FIG. 14 illustrates a graphical user interface of an exemplaryembodiment of a learning path with a smoothened path, depictingcontinuous course setting at each sub topic or topic level.

DETAILED DESCRIPTION OF THE INVENTION

Disclosed herein is a smart-learning and knowledge retrieval system(SLKRS) for providing adaptive and personalized e-learning based oncontinually, artificially learned unique characteristics of a knowledgeseeker. The computer-implemented method disclosed herein is an approachfor building an artificial intelligence-based knowledge representationand retrieval to answer queries from a user or another system. Themethod adapts and personalizes the learning process to each knowledgeseeker by generating personalized knowledge concept graphs for eachknowledge seeker. Through assessments and interactions, the SLKRScaptures in the knowledge concept graphs that the knowledge seekerknows, and how well the knowledge seeker can apply and understandconcepts. The SLKRS assists the knowledge seeker learn concepts that aredifficult for the knowledge seeker to comprehend, by connecting them tothe concepts that are readily understandable to the knowledge seeker.The SLKRS engages multiple interfaces, for example, conversationalchatbots, chat interfaces, text boxes, voice interfaces, phoneapplications, virtual reality interfaces and augmented realityinterfaces, to interact with the knowledge seeker. The SLKRS receivesqueries and feedback from the knowledge seeker and sends experiences tothe knowledge seeker through these interfaces. As used herein, an“experience” refers to a digital asset that the SLKRS generates fromstandard digital assets, for example, text, images, audios, videos,games, virtual reality simulations, and augmented reality simulations.Also, as used herein, a “digital asset” refers to text or media that isformatted into a binary source and includes the right to use it. TheSLKRS provides constant and immediate assistance to the knowledge seekerthrough the interfaces in an interactive manner. The SLKRS regularlygauges learning progress of the knowledge seeker through, for example,assessments, and continually adapts a learning path to make theknowledge seeker proficient in the concepts at the knowledge seeker'sown pace. The SKLRS generates a learning path for the knowledge seekeras a graphical output on a user interface, wherein the learning path'stransition points lead to projected learning path determined by theknowledge seekers observed performance over one or more of sub topics,topics and lessons. In an embodiment, the SLKRS is always available tothe knowledge seeker through an Internet connection.

FIG. 1 illustrates a computer-implemented method for providing adaptiveand personalized e-learning based on continually, artificially learnedunique characteristics of a knowledge seeker. The smart-learning andknowledge retrieval system (SLKRS) ingests 101 data from multiplesources in multiple formats. The sources and formats comprise theInternet, documents in multiple formats, social media streams, blogs,web pages, videos, audios, images, social media streams like Twitter®and Facebook®, Wikipedia®, games, virtual reality simulations, andaugmented reality simulations. The SLKRS merges 102 the ingested datainto a knowledge base to create an ontology. The SLKRS takes newinformation from the ingested data and combines it with existinginformation in an already created portion of the knowledge base. TheSLKRS builds an ontology for a knowledge domain. For example, if theknowledge domain of interest is applied computer science, then for thatarea, the SLKRS establishes a detailed hierarchy of terms in appliedcomputer science and their relationships. The SLKRS computes strengthsof terms found in each of the sources using parameters comprisingfrequency of occurrence of those terms in other sources, relevance ofthe terms to an already created portion of the ontology, and inputs fromthe knowledge seeker. Given one resource in the hierarchy, the SLKRSbuilds a graph of all related resources to a depth that is predeterminedand that can be updated. Through the graph, the SLKRS classifies allingested data against clusters of resources within the ontology,regardless of data format, based on the strength of the terms found inthe data source. The SLKRS also indexes and tags the data source basedon the strength of the terms contained in the data source. In anembodiment, the SLKRS assigns weights to the tags for data sources basedon the strengths of the terms found in them. In an embodiment, the SLKRSallocates numeric values to the tags and computes the strengths of theterms in a tagged data source as percentile ranks. The SLKRS assignsweights to the tags by multiplying the numeric values with numbersbetween 0 and 1 based on the percentile ranks indicated by the strengthsof the terms in the tagged data source. In an embodiment, the strengthof the terms in a data source is indicated by more than one percentilerank to account for the different parameters, namely, the frequency ofoccurrence of the terms in other sources, relevance of the terms to analready created portion of the ontology, and inputs from the knowledgeseeker, based on which the data source is evaluated.

In an embodiment, the smart-learning and knowledge retrieval system(SLKRS) builds a comprehensive ontology, complete with classificationand hierarchy, of science, technology, engineering, mathematics andarts, and multidisciplinary subjects. The SLKRS captures the connectionand relation between the different disciplines in multidisciplinarysubjects to capture context and ensure a smooth transition betweenconcepts without intervening gaps, and how they are connected andrelated. The ontology created from the merging of the ingested data bythe SLKRS allows the SLKRS to retrieve appropriate information inresponse to a query of a knowledge seeker after applying artificialintelligence and machine learning algorithms. In an embodiment, theSLKRS builds a repository of terms to describe subjects in the fields ofscience, technology, engineering, mathematics, and arts from ahierarchical approach. The SLKRS uses the built repository to organizethe fields of science, technology, engineering, mathematics, and artsinto sub areas and further keep drilling down to specific concepts thatcan be learned by knowledge seekers. So, when the SLKRS has furthercontent from the ingestion, the content can be tagged at the level ofthe specific concepts and when a knowledge seeker seeks for a piece ofknowledge, the hierarchical representation will allow the SLKRS to drilldown and retrieve content at various levels. Search and retrieval,classification of content, relationship of one piece of content toanother, relative position of content as to whether it is low level anddetailed, or it is higher level, etc., become possible with the ontologyand classification mechanism of the SLKRS. The SLKRS also usestaxonomy-based classification along with ontology, which iscategory-based classification. Taxonomy refers to tags which a knowledgeseeker can use to search for content or any information. Categorizationor ontology-based classification brings rigor and relationships amongcontent and subject areas and organizes the knowledge. The SLKRSimplements both taxonomy and category-based classification to provideinformation and results to knowledge seekers.

The smart-learning and knowledge retrieval system (SLKRS) assimilates103 the merged data to generate experiences. In the assimilate phase,the SLKRS processes the merged data into knowledge, and executes, via aprocessor, artificial intelligence and machine learning algorithms todevelop a thorough understanding of the merged information. The SLKRStags standard digital assets, for example, text, images, and video basedon relevant terms. The SLKRS generates the experiences as new assettypes from existing asset types, for example, videos. The SLKRS buildsexperiences from existing videos, games, virtual reality simulations,and augmented reality simulations that are immersive in nature. TheSLKRS builds the experiences to engage the knowledge seeker in a conceptthrough multiple senses as opposed to just spewing out text. The SLKRSlinks together all asset types in a huge graph, based on their proximityto weighted tags and search terms that a knowledge seeker might use tosearch for information in the SLKRS. The SLKRS generates decision treesto be able to parse the assimilated data to build the experiences. Adecision tree, analogous to flowcharts, begins at a root node andculminates in a leaf node with a decision. The SLKRS utilizes thedecision trees to arrive at decisions in building experiences for theknowledge seekers.

The smart-learning and knowledge retrieval system (SLKRS) receives 104 aquery from the knowledge seeker through one of multiple interfacescomprising text boxes, chat interfaces, voice interfaces, phoneapplications, interactive chat bots, virtual reality interfaces andaugmented reality interfaces. The SLKRS comprises a retrievalarchitecture that occurs as multiple layers. The first layer is a userinteraction layer, which provides a uniform interaction to the knowledgeseeker, an end user or a querying system, across all channels ofinteraction to the underlying SLKRS. It is through the user interactionlayer that the SLKRS receives the query from the knowledge seeker. Theknowledge seeker can choose to interact, for example, via Skype®,Facebook Messenger®, bots, smart phone applications, web applications,application programming interface (API) calls, virtual reality oraugmented reality applications, etc. In an embodiment, the SLKRSreceives the query through a translate layer comprising text to speech,speech to text, and language translations on receiving a request for thetranslate layer from the knowledge seeker. The translate layer is asoftware layer that provides text to speech and speech to textinteractions as well as language translations to the request queries. Inthe embodiment, the SLKRS implements the translate layer for use withall interactions with the knowledge seeker when the knowledge seekerrequests for the translate layer from the SLKRS through one of themultiple interfaces.

The smart-learning and knowledge retrieval system (SLKRS) retrieves 105a generated experience or an experience created based on an artificiallyintelligent understanding of the received query. In contrast to a simplerequest and response system that presents several links, pages, images,or videos in response to a request for information, the SLKRS is veryspecific and generates the most relevant response to the received query.The response is also a very specific and provides the relevant knowledgeto address the query. The SLKRS achieves the artificially intelligentunderstanding of the received query through artificial intelligence andmachine learning programs executable by at least one processorconfigured to execute computer program instructions. The retrievalarchitecture in the SLKRS comprises a natural language understandinglayer as the second layer after the user interaction layer. The SLKRSfilters all queries through a natural language understanding layerfirst. In this layer, the SLKRS understands the query using naturallanguage and determines the specific intent or intents behind the query.The SLKRS also determines entities or concepts of interest based on thereceived query. The SLKRS then compares the original question in thequery against the knowledge base to see if it matches a responsepattern. If the SLKRS finds such a direct match, then the SLKRS choosesthe result as the response since the question has an exact answer. Ifthere is no exact match determined to the original question, the SLKRSrephrases and simplifies the original question using the intents, theentities, and the concepts discovered by applying natural languageunderstanding. Thereafter, the SLKRS engages the third layer in theretrieval architecture, a pattern recognition layer. The SLKRS checksthe new set of simplified and rephrased questions against the knowledgebase using pattern recognition. If there is an exact match, the SLKRSchooses the result as the response.

The fourth layer in the retrieval architecture in the smart-learning andknowledge retrieval system (SLKRS) is a natural language processinglayer. If the techniques in the second and third layers fail to retrievea perfect match, then the SLKRS engages the natural language processinglayer to determine a close match based on previously trained data andconversations. If the SLKRS finds a result with accuracy and confidence,then the SLKRS chooses that result. If the natural language processinglayer fails to provide a response with a predetermined degree ofprobability, then the SLKRS engages a fifth layer in the retrievalarchitecture, a multi-learning layer. Using a variety of algorithms thathave been trained against models and fine-tuned, the SLKRS addresses thecurrent query and generates a response to the query. The SLKRS processesthe query in parallel via decision trees and decision paths, patternrecognition as well as natural language processing, or serially oneafter the other to see which method of understanding the query is abetter option for the query posed. The pre-generated decision trees thatthe SLKRS possesses after assimilation of the ingested data help theSLKRS process the query. For this, decision trees must exist for thattype of query or a sub-domain of the query. The SLKRS can recognizerepeating patterns and retrieve responses if a pattern matches exactly,and in the absence of a decision tree being available or patternmatching, can execute natural language processing to predict the query.

The smart-learning and knowledge retrieval system (SLKRS) sends 106 theretrieved experience to the knowledge seeker in an immersive formatthrough one or more of the interfaces for the knowledge seeker tointeract with the sent experience. The SLKRS presents an experience thatprovides the ability to interact in an immersive manner with theresponse for the knowledge seeker, an end user or a system. Theinteraction is also possible through a conversational interface like achat bot or a virtual reality or augmented reality interface. Thus, theend user can traverse the response and understand the responsecompletely.

The smart-learning and knowledge retrieval system (SLKRS) receives 107feedback from the knowledge seeker through one of the multipleinterfaces in response to the sent experience. For responses that theSLKRS generates using the natural language processing or multi-learningtechniques, the SLKRS also embeds the response with an option for theknowledge seeker to indicate if the chosen response is accurate or notaccurate. This allows the SLKRS to create a feedback loop that allowsthe SLKRS to contextually determine accuracy with cooperation from theknowledge seeker. Sometimes, the SLKRS determines accuracy from the nextthread of conversation from the knowledge seeker where the knowledgeseeker may seek additional clarifications or indicate dissatisfactionwith the response. The SLKRS uses artificial intelligence and machinelearning algorithms to figure out in the order in which a set of topicsis presented to the knowledge seeker.

In current learning methods, the table of contents is fixed and so arethe curriculum and examples taught to make a student understand aconcept. As such, there is no fluidity in the material that a studentlearns. In contrast, the smart-learning and knowledge retrieval system(SLKRS) provides a learning stream, which is, virtually, a single paneof glass where relevant material fluidly flows in and out of a knowledgeseeker's intellectual vision and range. The SLKRS customizes, viaartificial intelligence and machine learning algorithms, the learningstream that appears as a single pane of glass for each knowledge seekerbased on his or her individual characteristics. Consider, for example,reading a Physics book. As the knowledge seeker is reading a page andlooking at a problem, the knowledge seeker might not understand aformula. The knowledge seeker will seek and refer another basic bookthat may explain the formula. The knowledge seeker may face a problem inunderstanding the explanation of the formula and may then go online tograph sample results of the formula and look up related information. Inanother example, the knowledge seeker may have forgotten basic materialrelated to current studies and may look up definitions or class notes.In the learning stream provided by the SLKRS, the knowledge seeker'slearning needs are anticipated by the SLKRS and provided right at thejuncture where the knowledge seeker seeks additional information. Theknowledge seeker does not have to, for example, even go to anotherwebpage. The SLKRS utilizes artificial intelligence and machine learningalgorithms, understands the context of the knowledge seeker's currentstudies, the knowledge seeker's specific problem and brings data theknowledge seeker needs automatically right there to that very screen ofa computing device, for example, that the knowledge seeker is using toaccess the SLKRS.

The smart-learning and knowledge retrieval system (SLKRS) bringsrelevant information to the knowledge seeker automatically as the SLKRSrecognizes the knowledge seeker's specific issue. The SLKRS provides asubstantially personalized learning experience and adapts to theknowledge seeker's learning process continuously. The SLKRS adaptscontent that explains concepts a knowledge seeker is learning, and mayprovide new content for one knowledge seeker, while for anotherknowledge seeker learning the same concepts, the SLKRS may provideadditional content on the same topics to reinforce the understanding.For yet another knowledge seeker learning the same concepts, the SLKRSmay present related content that may have been poorly understood by theknowledge seeker in the past since that related content may befoundational to a current topic, and the issue with the knowledge seekerstruggling with the current topic may be due to the knowledge seeker nothaving understood a prior topic well enough.

The smart-learning and knowledge retrieval system (SLKRS) computes 108 ascore for the knowledge seeker continually based on each of the receivedquery and the received feedback. The artificial intelligence in theSLKRS takes into account a host of parameters to compute the score andadapt experiences for a knowledge seeker. The SLKRS considers factorscomprising attention, motivation, context, gender, age, grade level, andproficiency level to gauge their impact on the performance of theknowledge seeker in an e-learning course. The SLKRS artificially learnsthe unique characteristics of the knowledge seeker by consideringseveral attributes of the knowledge seeker through the knowledgeseeker's interaction with the SLKRS. One such attribute is thecoefficient of retention of the knowledge seeker. While knowledgeseekers, or students, learn at different rates and this learningefficiency could vary from concept to concept, the coefficient ofretention plays a part in student performance on a test. The coefficientof retention refers to the ability of a student to learn a new orrelated concept, and in general show continued interest in a course.

A student who attends an e-learning course regularly, and has a highcoefficient of retention, will perform well, gain more confidence, andhence will attend the e-learning course more regularly and this cyclecontinues. A student who is not able to attend regularly might start todo poorly and hence will start to lose interest, which will furtheraffect the interest level of the student in participating further inthis course. Aspects of concern in this scenario include identifying thefactors that affect interest levels of the student in the e-learningcourse, keeping interest levels at a certain threshold so that thestudent is motivated to come back to the course regularly, detectingdrops in interest levels and creating interventions at such junctures.The coefficient of retention, as used herein, is defined as the abilityof a student to recall a concept after the passage of a predeterminedamount of time and after being exposed to a predetermined number ofexamples or applications of that concept. The coefficient of retentionrho, for a student is expressed as a function of time spent by thestudent on a concept, t, and the number of applications of a conceptthat the student is exposed to, n, and takes on a value between 0 and 1.Specifically,

rho=f(t,n)

The smart-learning and knowledge retrieval system (SLKRS) computes rho,the coefficient of retention, for every student. The SLKRS computes thecoefficient of retention periodically for every student and once itfalls below a predetermined threshold, a student may have to repeat atopic or an entire lesson before being allowed to move on. However, thecoefficient of retention is only one of the inputs that the SLKRS usesto personalize the learning path as other measures of evaluation, forexample, regular assessments of knowledge acquired and direct feedbackfrom the knowledge seeker, are considered as well. During the knowledgeseeker's interaction with the SLKRS, the SLKRS continually assesses andcollects metrics based on, for example, clicks on a webpage displayed bythe SLKRS on a computing device that the knowledge seekers use to accessthe SLKRS. In an example, the SLKRS also continually assesses andcollects information on how long the knowledge seeker spends on a screenor frame or digital asset on the display of the computing device, whatactions the knowledge seeker takes, whether the knowledge seeker isrepeating a topic in an e-learning course, how much time has passedsince the knowledge seeker last accessed the particular e-learningcourse, etc. The SLKRS uses the knowledge seeker's interaction withitself to compute the score for that knowledge seeker.

Another attribute of a knowledge seeker that the smart-learning andknowledge retrieval system (SLKRS) uses to compute the score for theknowledge seeker is time. While some knowledge seekers, or students,learn one concept or lesson at a time, others learn in blocks or moveonto new concepts quickly. A student may like to complete lessons andsometimes will move on to more lessons spending hours at a time. On theother hand, another student may complete units of a lesson but notalways finish the whole lesson, as the student spends only about half anhour in one sitting. SLKRS considers assessing accuracy as a function oftime. For example, if a student spends 7 minutes answering 10 questionson a quiz and scores 9 points, and another student spends 5 minutesanswering 10 questions and scores 8 points, theoretically, given thatall other conditions remain the same for the two students, the SLKRSconsiders concluding that the second student could have scored the full10 points if the second student spent 7 minutes. If one were to take the“score” attribute separately, one might conclude that student 1 didbetter. But “speed” of answering questions reveals a mastery on aconcept and upon watching this attribute closely across several conceptsand quizzes, the SLKRS considers if the speed of answering questions isa better predictor of how fast a student might learn a related concept.The second student, who answers questions faster, might need fewerexamples and might accelerate learning. The first student is someone whomisses very few questions but takes more time to process information.The SLKRS considers whether the first student might need many moreexamples on the concept the student was tested on and related conceptsto be able to understand and progress further. The SLKRS also considersif the situation is related to motivation of the first student. TheSLKRS gauges if the second student portrays a tendency to rush if thesecond student feels very confident about the material and assumesanswers, and doesn't read instructions very well leading to mistakesbecause the second student didn't take time to answer the questions. Thefirst student may not attempt to answer a question unless the firststudent feels absolutely certain regarding the answer, and may take asmuch time as the first student needs to answer a question. Byconsidering the time taken by a knowledge seeker to achieve differentmilestones in an e-learning course, the SLKRS has one way of detectingthe mastery of the knowledge seeker.

Yet another attribute that the smart-learning and knowledge retrievalsystem (SLKRS) considers is experience level. A student may becomfortable with computers if he or she has already done someprogramming. For example, one student may have played with programmingwith Raspberry Pi® and Lego® Mindstorms, and may enjoy many games likeMinecraft® as gleaned by the SLKRS through the interactions of thestudent with the SLKRS. Another student may show less interest incomputer games, only playing something after watching someone else do itfirst, and then deciding if he or she is interested. As such, a relatedattribute that the SLKRS considers is confidence level. For example, onestudent may be very comfortable just jumping right in and learning newconcepts. The student may be content to explore and figure things out onhis or her own. On the other hand, another may be cautious and worryabout doing well showing signs of perfectionism. This student may oftenask parents for help. Another attribute is comfort with repetition. Forexample, while a student likes to do something over and over to feel heor she has mastered it, another student gets bored quickly withrepetition.

The smart-learning and knowledge retrieval system (SLKRS) also considerspreferences or aptitudes as an attribute. As deliberated on in the study“Development of an Adaptive Learning System with Multiple Perspectivesbased on Students' Learning Styles and Cognitive Styles” by Tzu-Chi Yanget al., the SLKRS considers cognitive styles and learning styles of theknowledge seekers in computing the score for the knowledge seekers.Cognitive style refers to an innate habitual approach to processinginformation when engaging in cognitive tasks. Cognitive styles aregenerally stable and consistent over time. Learning style refers to aninnate pattern of thinking, perceiving, problem solving, and rememberingwhen approaching a learning task. A learning style is fairly stable andconsistent over time and across a wide variety of learning situationsand is regarded as an application of a cognitive style to learningsituations. A learning style also refers to a preferred way of learningand concerns how students process information, how they learn and howwell they retain information.

The smart-learning and knowledge retrieval system (SLKRS) considers theimpact of a learning style to be neutral provided there is nocorrelation with other attributes because each learning style has itsown potential advantages and disadvantages. A certain learning style ofa knowledge seeker may help process information according to itsrelationship to the individual. Learning styles are not fixed and changebased on tasks or situations. Multiple classifications of learningstyles have been proposed over the last fifty years. Some of theminclude visual, auditory, and tactile learning styles, global andanalytic learning styles, and inductive and deductive learning styles.There is, however, a debate as to whether teaching a preferred styleenhances learning and leads to better outcomes. The validity of theimpact of instruction geared towards identified learner styles rangesfrom undetermined to questionable to low. Researchers cannotconsistently replicate and validate an interaction between a learner'sstyle and specific instructional methods, most likely due to variableswithin learner and/or instructional environments. Research, as found in“The Learning Styles and the Preferred Teaching—Learning Strategies ofFirst Year Medical Students” by Kharb et. al., “Learning styles andpedagogy in post-16 learning” by Coffield et. al., and “Visual,Auditory, Kinesthetic and Multimodal Learning” by University ofArkansas, Fort Smith, has explored learning styles. The researchconcludes that self-expressed preferred styles do not match performance,and that most learners are multi-modal, multi-situational, and adoptcorresponding strategies for learning. People do not learn the same wayand learning preferences are real, but it is not always appropriate toreduce to dichotomous groups. Learning styles are not mutually exclusivecategories but preferences may be mild, moderate, or strong. There areinnumerable individual variations when prior knowledge, experience, andskill level are factored into the learning style equation. Research alsoconcludes that the point is not to match teaching styles to learningstyles but rather to achieve balance, making sure that each stylepreference is addressed to a reasonable extent during instruction. Dr.Richard M. Felder opines that “learning styles provide no indication ofwhat the students are and are not capable of, nor are they legitimateexcuses for poor academic performance.” Students may have a learningpreference, but that is not the only way they can learn, nor should itbe the only way they are taught.

The smart-learning and knowledge retrieval system (SLKRS) takes intoaccount learning styles through characteristics learned from theknowledge seeker. It gauges if the knowledge seeker listens to andshares ideas with other knowledge seekers engaging with the SLKRS. Theknowledge seeker may perceive concretely and process reflectively andreflect on problems alone than brainstorm with others. The knowledgeseeker may view experiences from many perspectives, being insightful andpersonally involved in his or her own learning as opposed to being apassive recipient of instruction. The knowledge seeker may begoal-oriented, solitary, and logical. The knowledge seeker may perceiveabstractly and process reflectively, forming theories from concepts andprevious observations. A knowledge seeker may think sequentially. Aknowledge seeker may be actively involved in learning, thriving duringmanipulation of objects or when presented with problem to be solved,loving challenges. A knowledge seeker may be suited to active fieldstudy and excel in hands-on problem solving. A knowledge seeker may showa tendency to act without consulting others. A knowledge seeker may beinterested in self-discovery as identified based on the interaction withthe SLKRS. A knowledge seeker may be stimulating, impulsive,enthusiastic, and intuitive, avoiding isolation and seeking to energizeothers. A knowledge seeker may take on too much and not complete tasks.The SLKRS adapts the experiences and interventions it generates for aknowledge seeker based on such artificially learned uniquecharacteristics of the knowledge seeker.

The smart-learning and knowledge retrieval system (SLKRS) also considersmultiple intelligences portrayed by a knowledge seeker in his or herapproach in tackling a concept. Not everyone is smart in the same way;everyone possesses different faculties but to different degrees. Thetable below illustrates the characteristics of different intelligencesand consequences of possessing them to a high degree.

Intelligence Characteristics Consequences Verbal-linguistic facility inproducing favor using computer language technology and entering intodiscussions Musical sensitivity to components study by listening to ofmusic and emotional recordings related to implications topics Logic-reasoning deductively or prefer classifying, Mathematical inductively,and sequencing, and solving recognizing and problems manipulate abstractrelationships Spatial creating visual like to observe, imagine,representations of world and solve spatial and transfering them problemsmentally or concretely Kinesthetic use of body to solve excel withmanipulatives problems, making things, and prefer to participateconveying ideas and actively emotions Interpersonal working effectivelywith like to work in groups others and understanding and discuss withothers their emotions, goals, and intentions Intrapersonal understandingown like to work emotions, goals, and independently intentionsNaturalistic capacity to recognize and see patterns and like to makedistinctions in the identify a problem and natural world and use theresearch solutions ability productivelyKnowledge seekers benefit from following a specific learning strategy,namely, a chosen plan of action of how to approach a given learningtask. A knowledge seeker may deploy a learning strategy based on thenature of a task, prior experience, and motivation. Knowledge seekersare usually conscious of these strategies that they use. Knowledgeseekers also have learning preferences, namely, expressed personalpreferences favoring a type of learning environment and a method ofinstruction. For example, a learning preference may involve a preferencefor group or independent study. Knowledge seekers also possess learneraptitudes, namely, special innate capacities that give rise tocompetencies in dealing with specific types of content.

Furthermore, the smart-learning and knowledge retrieval system (SLKRS)considers other concepts of learning in computing the score for theknowledge seeker. Knowledge seekers could follow a holistic learningapproach, or an analytic learning approach. This pertains to the mannerin which individuals process information either as a whole, or brokendown into separate parts. Holists require explicit structure andguidance, external motivation, and social interaction. Some evidencepoints to holists being better in web-based learning environments thatprovide better structure and global perspective prior to deeper contentexploration and benefitting more from social interactions. Analytics areinternally directed, generate their own structure, and require lessexternal motivation and support. Some evidence points to analyticsperforming better in web-based learning environments that are lessstructured and promote in-depth content exploration prior to presentingoverviews.

Furthermore, the smart-learning and knowledge retrieval system (SLKRS)focuses on other learner characteristics. Prior knowledge isindisputably the biggest factor in predicting a learner's initialsuccess in almost every learning situation. Also, highly motivatedlearners will learn just about anything despite instructional design andexcel when instructional resources are readily available. Anothercharacteristic of relevance is perceived self-efficacy: if cognitiveresources are consumed with managing negative states associated with atask, learning will be negatively impacted. Anxiety, fear of failure,prior experiences with task or content to be learned, perceiveddifficulty of a task and other psychological factors also affect thelearning outcomes in an e-learning course.

The smart-learning and knowledge retrieval system (SLKRS) also computesa knowledge concept graph for the knowledge seeker to defineunderstanding and mastery over concepts in an e-learning course.Knowledge concept graphs can replace other measures of evaluation, forexample, a cumulative grade point average (CGPA), of the knowledgeseekers performance in the e-learning course. Starting from a high levelof the knowledge concept graph, one can drill down to particularsubjects or concepts and sub-concepts to gauge the mastery of theknowledge seeker in the particular concepts within the e-learningcourse. Hence, the subjects can be interdisciplinary and still itsunderstanding evaluated using the knowledge concept graphs. Moreover,the SLKRS computes a knowledge concept graph in real-time deriving thesame based on multiple underlying algorithms. In an embodiment, theSLKRS builds a knowledge concept graph that has nodes and edges. TheSLKRS builds this knowledge concept graph for each e-learning coursethat a knowledge seeker undertakes. In the embodiment, the SLKRS alsoaggregates and cumulatively starts developing a knowledge concept graphthat shows dots and connectors that, for example, look like synapses andneurons. In the embodiment, the dots, which represent concepts, arecolor coded to show mastery of concepts or no knowledge of concepts oranything in between. In the embodiment, the sizes of the dots showamount of knowledge around the corresponding concept. The connectorsshow which concepts are related. The thickness of the connectors areindicative of how strong the bonds are for a knowledge seeker betweenthe dots, that is, concepts that the connector connects. As such, overtime, the SLKRS pictures, via artificial intelligence and machinelearning algorithms, how these various interconnected concepts areworking within a knowledge seeker's brain and also how these conceptsstrengthen or weaken over time. The SLKRS, via the knowledge conceptgraph, develops a unique cognitive blueprint for each knowledge seeker.

The cognitive blueprint of a knowledge seeker captures theinterconnection of concepts, and the mastery or level of expertise ofthe knowledge seeker on a concept, for example, by assigning a score oneach node of the knowledge concept graph where each node represents aconcept and a leaf node represents a granular concept or a sub-conceptof a parent concept. The smart-learning and knowledge retrieval system(SLKRS) uses knowledge concept graphs to provide personalized learningpaths. The SLKRS combines knowledge concept graphs with an identifiedlearning style of the knowledge seeker to curate information related toconcepts in an e-learning course that the knowledge seeker undertakes.The SLKRS also uses the knowledge concept graphs to bring additionalexamples, content, and interventions to improve learning outcomes of theknowledge seeker.

The smart-learning and knowledge retrieval system (SLKRS) uses the scoreand the artificial understanding of direct feedback from the knowledgeseeker to tailor the immersive experiences that the SLKRS generates forthe knowledge seeker. The SLKRS considers association similar to thetheory of classical conditioning by the Russian physiologist IvanPavlov, and broaching related concepts and related examples to help theknowledge seeker better understand the context of the concept to belearned. Through the computation of the score and correspondingapplication of artificial intelligence, the SLKRS therefore artificiallylearns unique characteristics of the knowledge seeker for measuring anability of the knowledge seeker to learn and to show continued interestin an e-learning course. The SLKRS generates 109 a learning path for theknowledge seeker.

The smart-learning and knowledge retrieval system (SLKRS) providesanalytics for assessment of a knowledge seeker's progress and socialinteraction among stakeholders. In an embodiment where the SLKRS is usedby students, the SLKRS provides two types of groups—a student group anda friend circle. Parents, teachers, and students have their own logins,and each have a different type of landing page on a website. Parents andteachers have the ability to register, create a group or groups, buysubscriptions or courses, and assign students to the groups andsubscriptions purchased. In the embodiment, each student in a particulargroup needs to have the same subscription, for example, subscriptionnumber 2, which could be for students in grades 2 to 4. Individualcourses which are premium or free courses can be added to anysubscription. Students can also browse through courses or subscriptions,but instead of a “Buy” button, they will have a “Share” button on theirwebpages for them to share the course or subscription with their parentsfor the parents to review and buy for them. In an embodiment, parents,teachers and students have access to analytics, but what they see willdiffer based on role. Students can only see their own performance andanalytics data. Teachers can see the data for the entire group ofstudents they have added to a group. Parents also can see the analyticsdata for their own children.

In an embodiment, the other type of group that the smart-learning andknowledge retrieval system (SLKRS) provides is the friend circle. Thefriend circle concept is only for students. Each student can have afriend circle where the student can add a few select friends who arealso on the smart-learning and knowledge retrieval system (SLKRS) ascurrent students. Once friends are part of a friend circle, they will beable to share certain things within the circle and can stop sharing alsoat any time. So, students can be part of any group(s) where they wereadded by a parent or a teacher to the group(s), but a friend circle iscreated by the student. This creates a social aspect to SLKRS andpromotes virality. Students can also invite other friends who are not onSLKRS. Parents can also send an invite to other parents to join theSLKRS. The SLKRS also provides forums to allow students to postquestions and obtain answers from participants at large—parents,teachers, and other students. The SLKRS offers incentives forparticipation in the forums. Both posting questions and answers willresult in points that can be applied to prizes later. In an embodiment,a user interface for a student comprises a “share with friend”option—for a student who is older, and say, uses social media. Theycould use the share with a friend option to encourage their friend totry a class or with those they are friends with on Facebook; this caneven be used to recruit people to be in their friend circle.

The smart-learning and knowledge retrieval system (SLKRS) ensuressecurity of all communication and storage of data. The SLKRS handlessensitive information, for example, login credentials of knowledgeseekers and other stakeholders including parents, teachers, and users inan enterprise that uses the SLKRS, analytics, information of uniquecharacteristics of knowledge seekers, etc. When the various modules,services, or components of the SLKRS communicate within the SLKRS, andwhen the SLKRS communicates with third party services, knowledge seekersand other stakeholders, the SLKRS uses security measures. The SLKRSensures security of information through software mechanisms, forexample, data encryption, data masking, etc., and also through hardwaremechanisms, especially when enterprises are involved, for example,biometric security, disk encryption, etc. The SLKRS uses administrationpolicies to prevent data theft, safeguard copyright, and ensure privacyfor all the stakeholders.

FIG. 2 exemplarily illustrates a system 200 comprising thesmart-learning and knowledge retrieval system (SLKRS) 201 e implementedon an electronic device 201 for providing adaptive and personalizede-learning based on continually, artificially learned uniquecharacteristics of a knowledge seeker. In an embodiment, the SLKRS 201 euses programmed and purposeful hardware. The SLKRS 201 e is implementedon an electronic device 201, for example, a personal computer, a tabletcomputing device, a mobile computer, a portable computing device, alaptop, a touch device, a workstation, a server, portable electronicdevice, a network enabled computing device, an interactive networkenabled communication device, any other suitable computing equipment,combinations of multiple pieces of computing equipment, etc. In anembodiment, the computing equipment is used to implement applicationssuch as media playback applications, a web browser, an electronic mail(email) application, a calendar application, etc., with one or moreservers associated with one or more online services.

The smart-learning and knowledge retrieval system (SLKRS) 201 ecommunicates with client devices 202 exemplarily shown as a mobile phone202 a or a personal computer 202 b accessed by the knowledge seekerthrough a network 203. The network 203 is, for example, one of theinternet, an intranet, a wired network, a wireless network, acommunication network that implements Bluetooth® of Bluetooth Sig, Inc.,a network that implements Wi-Fi® of Wi-Fi Alliance Corporation, ageneral packet radio service (GPRS) network, a mobile telecommunicationnetwork such as a global system for mobile (GSM) communications network,a code division multiple access (CDMA) network, a third generation (3G)mobile communication network, a fourth generation (4G) mobilecommunication network, a fifth generation (5G) mobile communicationnetwork, a long-term evolution (LTE) mobile communication network, etc.,a local area network, a wide area network, an internet connectionnetwork, an infrared communication network, etc., or a network formedfrom any combination of these networks. The network 203 can be a wired,a wireless, or a combination of networks using different protocols. Inan embodiment, the smart-learning and knowledge retrieval system (SLKRS)201 e is accessible to the client devices 202, for example, through abroad spectrum of technologies and devices such as cellular phones,tablet computing devices, etc., with access to the internet.

The smart-learning and knowledge retrieval system (SLKRS) 201 ecommunicates with client devices 202 via the network 203, for example, ashort range network or a long range network. The client devices 202comprising, as exemplarily shown 202 a or 202 b, are electronic devices,for example, personal computers, tablet computing devices, mobilecomputers, mobile phones, smartphones, portable computing devices,personal digital assistants, laptops, wearable computing devices such asthe Google Glass® of Google Inc., the Apple Watch® of Apple Inc., etc.,touch centric devices, client devices, portable electronic devices,network enabled computing devices, interactive network enabledcommunication devices, any other suitable computing equipment,combinations of multiple pieces of computing equipment, etc. In anembodiment, the client devices 202 are hybrid computing devices thatcombine the functionality of multiple devices. Examples of a hybridcomputing device comprise a cellular telephone that includes a mediaplayer functionality, a gaming device that includes a wirelesscommunications capability, a cellular telephone that includes a documentreader and multimedia functions, and a portable device that has networkbrowsing, document rendering, and network communication capabilities.

As exemplarily illustrated in FIG. 2 , the smart-learning and knowledgeretrieval system (SLKRS) 201 e comprises a non-transitory computerreadable storage medium, for example, a memory unit 201 f for storingprograms and data, and at least one processor 201 a communicativelycoupled to the non-transitory computer readable storage medium. As usedherein, “non-transitory computer readable storage medium” refers to allcomputer readable media, for example, non-volatile media, volatilemedia, and transmission media, except for a transitory, propagatingsignal. Non-volatile media comprise, for example, solid state drives,optical discs or magnetic disks, and other persistent memory volatilemedia including a dynamic random access memory (DRAM), which typicallyconstitute a main memory. Volatile media comprise, for example, aregister memory, a processor cache, a random access memory (RAM), etc.Transmission media comprise, for example, coaxial cables, copper wire,fiber optic cables, modems, etc., including wires that constitute asystem bus coupled to the processor 201 d. The non-transitory computerreadable storage medium is configured to store computer programinstructions defined by modules, for example, 201 g, 201 h, 201 i, 201j, 201 k, etc., of the SLKRS 201 e. The modules 201 g, 201 h, 201 i, 201j, 201 k, 201 l, and 201 m are installed and stored in the memory unit201 f of the SLKRS 201 e. The memory unit 201 f is used for storingprogram instructions, applications, and data. The memory unit 201 f is,for example, a random access memory (RAM) or another type of dynamicstorage device that stores information and instructions for execution bythe processor 201 a. The memory unit 201 f also stores temporaryvariables and other intermediate information used during execution ofthe instructions by the processor 201 a. The SLKRS 201 e furthercomprises a read only memory (ROM) or another type of static storagedevice that stores static information and instructions for the processor201 a.

The processor 201 a is configured to execute the computer programinstructions defined by the modules, for example, 201 g, 201 h, 201 i,201 j, 201 k, etc., of the smart-learning and knowledge retrieval system(SLKRS) 201 e. The processor 201 a refers to any of one or moremicroprocessors, central processing unit (CPU) devices, finite statemachines, computers, microcontrollers, digital signal processors, logic,logic devices, user circuits, application specific integrated circuits(ASIC), field-programmable gate arrays (FPGA), chips, etc., or anycombination thereof, capable of executing computer programs or a seriesof commands, instructions, or state transitions. In an embodiment, theprocessor 201 a is implemented as a processor set comprising, forexample, programmed microprocessors and math or graphics co-processors.The processor 201 a is selected, for example, from the Intel® processorssuch as the Itanium® microprocessor or the Pentium® processors, AdvancedMicro Devices (AMD®) processors such as the Athlon® processor,UltraSPARC® processors, microSPARC® processors, HP® processors,International Business Machines (IBM®) processors such as the PowerPC®microprocessor, the MIPS® reduced instruction set computer (RISC)processor of MIPS Technologies, Inc., RISC based computer processors ofARM Holdings, Motorola® processors, Qualcomm® processors, etc. The SLKRS201 e disclosed herein is not limited to employing a processor 201 a. Inan embodiment, the SLKRS 201 e employs controllers or microcontrollers.

As exemplarily illustrated in FIG. 2 , the electronic device 201comprising the smart-learning and knowledge retrieval system (SLKRS) 201e further comprises a data bus 201 u, a network interface 201 o, aninput/output (I/O) controller 201 p, input devices 201 q, a fixed mediadrive 201 r such as a hard drive, a removable media drive 201 s forreceiving removable media, output devices 201 t, etc. The data bus 201 upermits communications between the modules, for example, 201 g, 201 h,201 i, 201 j, 201 k, etc., of the SLKRS 201 e and other components ofthe electronic device 201. The network interface 2010 enables connectionof the SLKRS 201 e to the network 203. In an embodiment, the networkinterface 2010 is provided as an interface card, also referred to as aline card. The network interface 2010 comprises, for example, one ormore of an infrared (IR) interface, an interface implementing Wi-Fi® ofWi-Fi Alliance Corporation, a universal serial bus (USB) interface, aFireWire® interface of Apple Inc., an Ethernet interface, a frame relayinterface, a cable interface, a digital subscriber line (DSL) interface,a token ring interface, a peripheral controller interconnect (PCI)interface, a local area network (LAN) interface, a wide area network(WAN) interface, interfaces using serial protocols, interfaces usingparallel protocols, Ethernet communication interfaces, asynchronoustransfer mode (ATM) interfaces, a high speed serial interface (HSSI), afiber distributed data interface (FDDI), interfaces based on atransmission control protocol (TCP)/internet protocol (IP), interfacesbased on wireless communications technology such as satellitetechnology, radio frequency (RF) technology, near field communication,etc. The I/O controller 201 r controls input actions and output actionsperformed by the SLKRS 201 e.

The display screen 201 b, via the graphical user interface (GUI) 201 c,displays content of files, display interfaces, user interface elementssuch as chat windows, etc. The display screen 201 b is, for example, avideo display, a liquid crystal display, a plasma display, an organiclight emitting diode (OLED) based display, etc. The smart-learning andknowledge retrieval system (SLKRS) 201 e provides the GUI 201 c on thedisplay screen 201 b. The GUI 201 c is, for example, an online webinterface, a web based downloadable application interface, a mobilebased downloadable application interface, etc. The display screen 201 bdisplays the GUI 201 c. The input devices 201 q are used for inputtingdata into the SLKRS 201 e for routine maintenance of the SLKRS 201 e.The input devices 201 q are, for example, a keyboard such as analphanumeric keyboard, a microphone, a joystick, a pointing device suchas a computer mouse, a touch pad, a light pen, a physical button, atouch sensitive display device, a track ball, a pointing stick, anydevice capable of sensing a tactile input, etc. The output devices 201 toutput the results of operations performed by the SLKRS 201 e.

The processor 201 a is configured to execute computer programinstructions defined by modules of the smart-learning and knowledgeretrieval system (SLKRS) 201 e. The modules of the SLKRS 201 e comprisean ingestion module 201 g, a merge module 201 h, a computation module201 i, an assimilation module 201 j, a data transfer module 201 k, aretrieval module 201 l, and an intervention module 201 m, learningmanagement module 201 p and AI ML services module 201 q. The learningmanagement module 201 p consists of several functions provided todeliver eLearning courses in multiple content formats, immersivelearning environments to learn emerging STEM fields, ability to writecode and test code, assessments, reporting, user management, subscribermanagement, ability for parents, students, and educators to access thesystem, community and collaboration. The AI ML module 201 q consists ofAI algorithms that are available on-demand and in batch mode and can beinvoked via micro services, http endpoints, or by any other means a userrequests access to the AI algorithms included and provided access to auser or an external system or external service. Knowledge Concept GraphRetention, Alternate Learning Path, Survival Analysis, Optimum LearningTime, Learning Loss Computation, and many other AI ML algorithms areavailable under the AI ML services module. The ingestion module 201 gingests data from multiple sources in multiple formats. The multiplesources in multiple formats comprise the Internet, documents in aplurality of formats, social media streams, blogs, web pages, videos,audios, and images. The merge module 201 h merges the ingested data intoa knowledge base to create an ontology. The ingested data is merged intothe knowledge base based on strengths of terms found in the sources fromwhich the data is ingested, where the strengths of the terms arecomputed by the computation module 201 i. The merge module 201 h indexesand tags the sources from which the data is ingested based on thecomputed strengths of terms found in the sources. The assimilationmodule 201 j assimilates the merged data to generate experiences fromthe assimilated data. The data transfer module 201 k receives a queryfrom a knowledge seeker through one of multiple interfaces via thegraphical user interface (GUI) 201 c on the electronic device 201. Themultiple interfaces comprise text boxes, chat interfaces, voiceinterfaces, phone applications, interactive chat bots, virtual realityinterfaces and augmented reality interfaces. The retrieval module 201 lretrieves a generated experience or an experience created based on anartificially intelligent understanding of the received query. Theretrieval module 201 l achieves artificially intelligent understandingof the received query through artificial intelligence and machinelearning programs that are executable by the processor 201 a. The datatransfer module 201 k sends the retrieved experience to the knowledgeseeker in an immersive format via one or more of the multiple interfacesfor the knowledge seeker to interact with the sent experience. The datatransfer module 201 k also receives feedback from the knowledge seekervia the one of the interfaces in response to the sent experience. Thedata transfer module receives the query and the feedback from theknowledge seeker, and sends the retrieved experience to the knowledgeseeker through a translate layer comprising text to speech, speech totext, and language translations on receiving a request for the translatelayer from the knowledge seeker. The translate layer is disclosed in thedetailed description of FIG. 1 .

The computation module 201 i of the smart-learning and knowledgeretrieval system (SLKRS) 201 e computes the strengths of the terms foundin each source using parameters comprising frequency of occurrence ofthe terms in the other plurality of sources, relevance of the terms to acreated portion of the ontology, and the queries and the feedbackreceived from the knowledge seeker. The computation module 201 icomputes a score for the knowledge seeker continually based on thereceived query and the received feedback, thereby artificially learningunique characteristics of the knowledge seeker for measuring an abilityof the knowledge seeker to learn and to show continued interest in ane-learning course. The computation module 201 i computes a coefficientof retention for the knowledge seeker based on a test of the ability ofthe knowledge seeker to recall a concept after the passage of apredetermined length of time and after being exposed to a predeterminednumber of applications of that concept. The computation module 201 icomputes a knowledge concept graph for the knowledge seeker to defineunderstanding and mastery over concepts in the e-learning course. Theintervention module 201 m generates interventions and improvedexperiences to provide adaptive and personalized e-learning to theknowledge seeker based on the score for the knowledge seeker computed bythe computation module 201 i.

The database 2010 and the one or more client databases 201 h of thesmart-learning and knowledge retrieval system (SLKRS) 201 e can be anystorage areas or media that can be used for storing data and files. Inan embodiment, the SLKRS 201 e stores the received feedback in externaldatabases, for example, a structured query language (SQL) data store ora not only SQL (NoSQL) data store such as the Microsoft® SQL Server®,the Oracle® servers, the MySQL® database of MySQL AB Company, themongoDB® of MongoDB, Inc., the Neo4j graph database of Neo TechnologyCorporation, the Cassandra database of the Apache Software Foundation,the HBase™ database of the Apache Software Foundation, etc. In anotherembodiment, the database 2010 and the one or more client databases 201 hcan be locations on a file system. In another embodiment, the database2010 and the one or more client databases 201 h can be remotely accessedby the SLKRS 201 e via the network 203. In another embodiment, thedatabase 2010 and the one or more client databases 201 h are configuredas cloud-based databases implemented in a cloud computing environment,where computing resources are delivered as a service over the network203.

Computer applications and programs are used for operating the modules ofthe smart-learning and knowledge retrieval system (SLKRS) 201 e. Theprograms are loaded onto the fixed media drive 201 r and into the memoryunit 201 f of the SLKRS 201 e via the removable media drive 201 s. In anembodiment, the computer applications and programs are loaded directlyon the SLKRS 201 e via the network 203. The processor 201 a executes anoperating system, for example, the Linux® operating system, the Unix®operating system, any version of the Microsoft® Windows® operatingsystem, the Mac OS of Apple Inc., the IBM® OS/2, VxWorks® of Wind RiverSystems, Inc., QNX Neutrino® developed by QNX Software Systems Ltd., thePalm OS®, the Solaris operating system developed by Sun Microsystems,Inc., etc. The SLKRS 201 e employs the operating system for performingmultiple tasks. The operating system is responsible for management andcoordination of activities and sharing of resources of the SLKRS 201 e.The operating system further manages security of the SLKRS 201 e,peripheral devices connected to the SLKRS 201 e, and networkconnections. The operating system employed on the SLKRS 201 erecognizes, for example, inputs provided by a user of the SLKRS 201 eusing one of the input devices 201 q, the output devices 201 t, files,and directories stored locally on the fixed media drive 201 r. Theoperating system on the SLKRS 201 e executes different programs usingthe processor 201 a. The processor 201 a and the operating systemtogether define a computer platform for which application programs inhigh level programming languages are written.

The processor 201 a retrieves instructions defined by the ingestionmodule 201 g, the merge module 201 h, the computation module 201 i, theassimilation module 201 j, the data transfer module 201 k, the retrievalmodule 201 l, and the intervention module 201 m for performingrespective functions disclosed above. The processor 201 a retrievesinstructions for executing the modules, for example, 201 g, 201 h, 201i, 201 j, 201 k, etc., of the smart-learning and knowledge retrievalsystem (SLKRS) 201 e from the memory unit 201 f. A program counterdetermines the location of the instructions in the memory unit 201 f.The program counter stores a number that identifies the current positionin the program of each of the modules, for example, 201 g, 201 h, 201 i,201 j, 201 k, etc., of the SLKRS 201 e. The instructions fetched by theprocessor 201 a from the memory unit 201 f after being processed aredecoded. The instructions are stored in an instruction register in theprocessor 201 a. After processing and decoding, the processor 201 aexecutes the instructions, thereby performing one or more processesdefined by those instructions.

At the time of execution, the instructions stored in the instructionregister are examined to determine the operations to be performed. Theprocessor 201 a then performs the specified operations. The operationscomprise arithmetic operations and logic operations. The operatingsystem performs multiple routines for performing a number of tasksrequired to assign the input devices 201 q, the output devices 201 t,and the memory unit 201 f for execution of the modules, for example, 201g, 201 h, 201 i, 201 j, 201 k, etc., of the smart-learning and knowledgeretrieval system (SLKRS) 201 e. The tasks performed by the operatingsystem comprise, for example, assigning memory to the modules, forexample, 201 g, 201 h, 201 i, 201 j, 201 k, etc., of the SLKRS 201 e andto data used by the SLKRS 201 e, moving data between the memory unit 201f and disk units, and handling input/output operations. The operatingsystem performs the tasks on request by the operations and afterperforming the tasks, the operating system transfers the executioncontrol back to the processor 201 a. The processor 201 a continues theexecution to obtain one or more outputs. The outputs of the execution ofthe modules, for example, 201 g, 201 h, 201 i, 201 j, 201 k, etc., ofthe SLKRS 201 e are displayed to a user of the SLKRS 201 e on the outputdevice 201 t.

In an embodiment, one or more portions of the SLKRS 201 e aredistributed across one or more computer systems (not shown) coupled tothe network 203. While the SLKRS 201 e presents a seamless experience toa knowledge seeker interacting with the SLKRS 201 e, the database 201 o,of the SLKRS 201 e, is spread over multiple computer systems located atvarious locations globally, as a distributed database, for quicker andsmoother access by knowledge seekers situated at different geographicallocations. In another embodiment, the database 2010 is implemented as agraph database with nodes for data and relationships indicatingassociation between the data in various nodes of the graph database. Inanother embodiment, the database 2010 utilizes features of differentdatabase systems, for example, relational databases and graph databases.

The non-transitory computer readable storage medium disclosed hereinstores computer program codes comprising instructions executable by atleast one processor 201 a for providing adaptive and personalizede-learning based on continually artificially learned uniquecharacteristics of a knowledge seeker. The computer program codescomprise a first computer program code for ingesting data from multiplesources in multiple formats; a second computer program code for mergingthe ingested data into a knowledge base to create an ontology, where theingested data is merged into the knowledge base based on computedstrengths of terms found in the sources from which the data is ingested;a third computer program code for assimilating the merged data togenerate experiences from the assimilated data; a fourth computerprogram code for receiving a query from a knowledge seeker through oneof multiple interfaces; a fifth computer program code for retrieving agenerated experience or an experience created based on an artificiallyintelligent understanding of the received query; a sixth computerprogram code for sending the retrieved experience to the knowledgeseeker in an immersive format through one or more of the interfaces forthe knowledge seeker to interact with the sent experience; a seventhcomputer program code for receiving feedback from the knowledge seekerthrough one of the interfaces in response to the sent experience; aneight computer program code for computing a score for the knowledgeseeker continually based on the received query and the receivedfeedback, thereby artificially learning unique characteristics of theknowledge seeker for measuring an ability of the knowledge seeker tolearn and to show continued interest in an e-learning course; and aninth computer program code for generating interventions and improvedexperiences to provide adaptive and personalized e-learning to theknowledge seeker based on the computed score for the knowledge seeker.

The computer program codes further comprise one or more additionalcomputer program codes for performing additional steps that may berequired and contemplated for providing adaptive and personalizede-learning based on continually artificially learned uniquecharacteristics of a knowledge seeker. In an embodiment, a single pieceof computer program code comprising computer executable instructionsperforms one or more steps of the method disclosed herein for providingadaptive and personalized e-learning based on continually artificiallylearned unique characteristics of a knowledge seeker. The computerprogram codes comprising computer executable instructions are embodiedon the non-transitory computer readable storage medium. The processor201 a retrieves these computer executable instructions and executesthem. When the computer executable instructions are executed by theprocessor 201 a, the computer executable instructions cause theprocessor 201 a to perform the steps of the method for of the processingcomputer server 201 c.

FIG. 3 exemplarily illustrates a microservice architecture that thesmart-learning and knowledge retrieval system (SLKRS) 201 e utilizes forproviding microservices to knowledge seekers. In the microservicearchitecture, the individual functionalities of the SLKRS 201 e areclearly delineated and modularized into individual services calledmicroservices 304. Such modularity enables independent development,deployment, troubleshooting, and scaling of the different services 304.This is in contrast to a traditional programming model involving amonolithic architecture where elements of a software program areinterwoven and interdependent. The client 301 refers to a browser,application, device, etc., of a knowledge seeker who registers and onceregistered signs into an account in the SLKRS 201 e. The sign in processof the client 301 and subsequent user authentication to make sure theclient 301 requesting access to the SLKRS 201 e is indeed representingthe knowledge seeker who had registered using the concerned sign ininformation is done by an identity provider 302. The identity provider302 is a system that manages identity information of clients includingregistration, authentication, and management, for example, changing apassword, handling forgotten passwords, etc. The application programminginterface (API) gateway 303 handles requests from clients 301 for accessof services 304 of the SLKRS 201 e. The API gateway 303 is the interfacebetween the client 301 and the services 304 of the SLKRS 201 e, androutes a request from the client 301 to one or more services 304 asrequired with necessary translation between protocols, for example,REST, SOAP, JSON, XML, or between any other protocol used by a webpageor application that the client 301 uses to access the SLKRS 201 e and aninternal protocol the SLKRS 201 e uses for its services 304. The client301 will be able to directly access some of the services 304 through theAPI gateway 303 and will need to be authenticated first through theidentity provider 302 to access some other services 304 that need alogin. The services 304 themselves comprise concept mastery assessments,social interaction, dictionary, jokes and quotes, trivia, etc., that theSLKRS 201 e offers to the client 301 after logging in to the SLKRS 201e. The remote service 305 is a process that dwells away from theapplication server comprising the instance of the SLKRS 201 e underconsideration and which provides a service, for example, a web serviceor a caching service, to the SLKRS 201 e.

In the microservice architecture illustrated in FIG. 3 , servicediscovery 306 refers to the detection of network locations of instancesof services that, for example, have dynamically assigned networklocations or are situated on different servers. The smart-learning andknowledge retrieval system (SLKRS) 201 e, in an embodiment, uses serversthat are located at different geographical locations and that havecached instances of services and content to enable faster access of theservices and content for knowledge seekers located at differentgeographical locations. The management 307 module handles the managementof the services 304 and their application components. For example,management involves access management of the services 304 based on thetype of client 301, for example, a student, parent, or a teacheraccessing the services. The management 307 module can also configure theservices 304 in real time and manage a lifecycle of the servicesincluding, for example, auto-scaling, namely creating more instances ofthe services 304 in response to increased demand, and auto-healing,namely recognizing and reestablishing failed instances of the services304. Static content 308 comprises digital assets and information that isingested, merged, and assimilated by the SLKRS 201 e. The contentdelivery network (CDN) 309 is a network of distributed servers that arelocated at different geographic locations to provide quicker responsesto clients 301 located at or near those geographic locations.

FIG. 4 exemplarily illustrates a block diagram of an artificialintelligence microservices platform of the smart-learning and knowledgeretrieval system (SLKRS) 201 e. A knowledge seeker, that is a client 301as illustrated in FIG. 3 can access the SLKRS 201 e through a userinterface (UI) or user experience (UX) portal 401 comprising multipleinterfaces, for example, a web portal, a mobile application, a chatinterface, etc. as disclosed in the detailed description of FIG. 1 .Reports and insights 402 are informational data comprising specific datafrom the artificially learned unique characteristics of the knowledgeseeker that the SLKRS 201 e uses for measuring an ability of theknowledge seeker to learn and to show continued interest in ane-learning course. The SLKRS 201 e makes available to the knowledgeseeker, the specific data that shows the progress of the knowledgeseeker in the e-learning course, and insights that the knowledge seekermay draw on for improvement and greater self-awareness in learning. Thechatbot platform 403 is a software platform that receives queries andfeedback from the knowledge seeker and provides responses includingexperiences to the knowledge seeker based on an artificially intelligentunderstanding of the received query, through a translate layer ifrequested by the knowledge seeker, as disclosed in the detaileddescription of FIG. 1 . Domain specific microservices 404 aremicroservices that have clearly delineated boundaries and definedcontexts with their own requirements and criteria segregating them fromother microservices that the SLKRS 201 e might invoke for multiplerequirements. For example, self-service for a client 301 as illustratedin FIG. 3 , involving identity management is a context that can beseparated from other requirements of the SLKRS 201 e. The domainspecific microservices 404 could be created jointly with an enterpriseusing the SLKRS 201 e to cater to specific requirements of theenterprise above and beyond the services offered by the SLKRS 201 e. TheSLKRS 201 e can also include legacy applications and services of theenterprise when they are transformed to microservices. Generalmicroservices 405 and artificial intelligence (AI) microservices 406 arethe microservices that the SLKRS 201 e provides to knowledge seekersapart from the domain specific microservices 404. The microservicesplatform 407 is an environment comprising program code that themicroservices run in. The microservices platform 407 underlies theprograms constituting the microservices and provides the necessaryfunctionalities that are used to build the microservices. Caching 408comprises duplication of data and instances of the microservices forspeedier access of the data and microservices by different components ofthe SLKRS 201 e to provide the microservices to the knowledge seeker.

Knowledge graphs 409 are the knowledge concept graphs that thesmart-learning and knowledge retrieval system (SLKRS) 201 e computes fora knowledge seeker to define his or her understanding and mastery overconcepts in an e-learning course as disclosed in the detaileddescription of FIG. 1 . Analytics 410 refers to the information that theSLKRS 201 e derives from a systematic analysis of results of assessmentsand progress of a knowledge seeker through the e-learning course andstatistics related to the same. The service database 411 comprises thepersistent data that microservices store for their operations. Differentmicroservices may have different data storage requirements, for example,a relational database for some microservices and a NoSQL database forsome others. Moreover, modularity of microservices is best served when adatabase of a microservice is accessible only by that microservice. Assuch, multiple databases are stored in the service database 411 to caterto the data storage needs of the different microservices. The recordstore 412 is a data storage repository for storing the records of theknowledge seekers for deriving the analytics 410 and for the knowledgeseekers to keep a record of their learning endeavors. The message bus413 is a common interface with a common set of commands and datarepresentation that enables seamless communication between the differentcomponents of the SLKRS 201 e. Security 414 is an integral part of allcommunication within and outside the SLKRS 201 e as disclosed in thedetailed description of FIG. 1 .

FIG. 5 exemplarily illustrates the internals of the microservicesplatform of the smart-learning and knowledge retrieval system (SLKRS)201 e. The microservices platform 407 in FIG. 5 refers to themicroservices platform 407 disclosed in the detailed description of FIG.4 . The microservices platform 407 encompasses management, maintenance,security, testing, and deployment of services, and communication betweenthe services and with other components, for example, databases and APIgateway, of the SLKRS 201 e. The services container 501 holds theservices and is implemented, for example, as an array object comprisingthe services. The services container 501 comprises a registry withversioning of the services, documentation pertaining to the services,pooling of services to be called upon for a task, and ensuring securityof data and access pertaining to each service through a centralizedsecurity interceptor when multiple services are called upon by tasks setin motion through requests to the SLKRS 201 e by a knowledge seeker. Thesecurity framework 502 deals with authentication of users of the SLKRS201 e, namely the knowledge seekers, authorization of access to servicesand data, and the corresponding generation and verification of leasetokens granting the access. Services deployment 503 handles theautomated deployment of services to be ready to run on servers after theservices are updated or modified. Test automation 504 concernsautomation of the testing of services at various points and raising offlags when some test cases return unexpected results on running theservices. Caching and cache management 505 refers to the duplication ofdata at different points in the SLKRS 201 e including various instancesof the services to ensure speedy access to data. Core messaginginfrastructure 506 handles messaging between the services andcommunication with the API gateway 303 exemplarily illustrated in FIG. 3and includes notification services, intelligent routing of data, andpush notifications ensuring automatic updates on changes. In anembodiment, the core messaging infrastructure 506 is an asynchronousmessaging system, wherein the services do not have to wait for immediateresponses from the recipient of their sent messages and can completemessaging process through a one-way communication. The messaginginfrastructure 506 delivers the responses to the sender of the initialmessages when available from the recipient.

Infrastructural services 507, as exemplarily illustrated in FIG. 5 ,concerns maintenance of the infrastructure of the services platform 501and comprises logging of events, for example, access requests, andmanagement of exceptions or errors as and when they arise, mediationbetween services, for example, when more than one service requestsresources or common data. Infrastructural services 507 also compriserepresentational state transfer (REST) discovery to capture trafficfrom, to, and between services conforming to the constraints set by theREST architecture from a browser or a software client accessing theservices and a directory interface enabling accessing data and resourcesthrough a name look up. Services management 508 concerns monitoring andinstrumentation of the services on the service platform 501 to measureperformance, monitor execution, and diagnose errors in the running ofthe services. Performance and quality of service 509 ensures bestperformance and user experience when accessing the services over theInternet or an intranet by minimizing non-essential interactions withand between services that might hinder optimal network performance. Theservices platform 501 also comprises data mapping, data transformation,and orchestration 510. Data mapping and transformation ensurescompatibility of data between services and between the services platform501 and the client 301 exemplarily illustrated in FIG. 3 . Thisincludes, for example, correlating sensitive data between masked datareceived from the client 301 via the identity provider 302 and the APIgateway 303 exemplarily illustrated in FIG. 3 with a database entry of aservice. Orchestration refers to the coordination and management of dataand resources to align with the performance and quality of service 509.

FIG. 6 exemplarily illustrates an example of the microservices that thesmart-learning and knowledge retrieval system SLKRS 201 e provides toknowledge seekers. The microservices platform 407 in FIG. 6 refers tothe microservices platform 407 disclosed in the detailed description ofFIG. 4 . FIG. 6 illustrates examples of the general microservices 405and artificial intelligence (AI) microservices 406 exemplarilyillustrated in FIG. 4 . Service registry, as depicted under servicecontainer 501 in FIG. 5 , and service management 508 are as disclosed inthe detailed description of FIG. 5 . General microservices 405 comprisejokes and quotes 601 and trivia 602 to refresh and motivate theknowledge seekers. Concept mastery detection 603 corresponds to regularassessments of the knowledge seekers' understanding of concepts in ane-learning course. User engagement 604 refers to the engagement of theknowledge seekers in the e-learning course. The SLKRS 201 e generatesinterventions to maintain interest levels of the knowledge seekers toensure their optimum performance in learning. The SLKRS 201 e alsoprovides dictionary and language 605 support to ensure knowledge seekersare supported regardless of the language they speak, except maybe in thecase of esoteric languages. The SLKRS 201 e also provides predictperformance 606 microservices to predict the performance of theknowledge seekers to provide an insight into their projected learningpath should they proceed in the same manner as they are at the time. Theartificial intelligence services 406 comprise a behavior engine 607 anda sentiment and emotion engine 608 that use machine learning andartificial intelligence algorithms to analyze the knowledge seekers'learning characteristics. The SLKRS 201 e uses the outputs of thebehavior engine 607 and the sentiment and emotion engine 608 to measurean ability of each knowledge seeker to learn and to show continuedinterest in an e-learning course. The SLKRS 201 e generatesinterventions and improved experience for the knowledge seekers usingthe outputs of the behavior engine 607 and the sentiment and emotionengine 608. The social interaction service 609 handles interaction amongknowledge seekers or students and all stakeholders comprising parentsand teachers as well. In an embodiment, the social interaction service609 also links to social media websites, for example, Twitter® andFacebook, to allow seamless interaction between the stakeholders throughthe SLKRS 201 e and social media websites. The caching service 610caches or duplicates storage of data at convenient memory locations onthe electronic device to allow speedier access to the data by thecomponents of the SLKRS 201 e. The messaging service 611 handlesmessages that the SLKRS 201 e sends to a knowledge seeker by way ofinterventions and feedback that the knowledge seeker gives to the SLKRS201 e. The content analysis service 612 analyzes e-learning contentassimilated by the SLKRS 201 e and responses of the knowledge seeker toassessments in light of any feedback from the knowledge seeker that themessaging service 611 receives to enable the SLKRS 201 e to provideimproved experiences to the knowledge seeker. The artificialintelligence as a service (AIaaS) platform 613 offers the generalmicroservices 405 and artificial intelligence (AI) 406 microservices tothe other components of the SLKRS 201 e for using those services toprovide adaptive and personalized e-learning to the knowledge seekers.In an embodiment, the AIaaS platform 613 is available to an enterprisethat uses the SLKRS 201 e for e-learning. In the embodiment, theenterprise can use the AIaaS platform 613 of the SLKRS 201 e to run orrefine its legacy applications and services, and also to offer newcustomized services to its clients.

FIG. 7 exemplarily illustrates a chatbot platform as an example of aninterface that the smart-learning and knowledge retrieval system (SLKRS)201 e provides to knowledge seekers for their interaction with thesmart-learning and knowledge retrieval system. The chatbot platform 403is part of the microservices platform 407 disclosed in the detaileddescription of FIG. 4 . The chatbot platform 403 provides interactionbetween a user 701, who is a knowledge seeker, a parent, a teacher, oranother stakeholder, for example, a user in an enterprise using theSLKRS 201 e, and a chatbot 702. The chatbot platform 407 allows multiplechannels of engagement 703 comprising, for example, the World Wide Web,social media, mobile applications, Skype®, Facebook Messenger®, chatprograms, etc., for the knowledge seekers and stakeholders to interactwith the SLKRS 201 e. The sentiment and emotion engine 608 is disclosedin the detailed description of FIG. 6 . The chatbot platform 403provides quizzes and assessments 704 related to the e-learning course tothe knowledge seekers. These assessments are interactive in naturecompared to plain assignments or tests that students take up in acourse. The chatbot platform 403 sources the quizzes and assessments 704from the microservices platform 407 through the concept masterydetection 603 service under general microservices 405 as disclosed inthe detailed descriptions of FIG. 4 and FIG. 6 .

The chatbot platform 403 also comprises the translate layer 705disclosed in the detailed description of FIG. 1 . The translate layercomprises text to speech and speech to text services, and a translationapplication programming interface (API), which provides translationservices when requested by other services of the smart-learning andknowledge retrieval system (SLKRS) 201 e and ultimately by a knowledgeseeker. The chatbot platform 403 integrates third-party services, forexample, weather API, Google® search, Wikipedia, dictionary services,and other custom services, into its responses to the user and queriesfrom the user through the integration module 706. For example, thechatbot platform 403 can pull up relevant information pertaining to aquery of a knowledge seeker from the third-party services in addition tothe experiences from the assimilated knowledge that the SLKRS 201 eprovides to the knowledge seeker. The chatbot platform 403 comprises apattern recognition module 707, a natural language processing (NLP)module 708, and a deep learning module 709 that correspond to the third,fourth, and fifth layers of the retrieval architecture of the SLKRS 201e as disclosed in the retrieval step of the detailed description of FIG.1 . As disclosed in the detailed description of FIG. 1 , the patternrecognition module 707 checks simplified and rephrased questions againsta knowledge base 710 using pattern recognition, and the NLP module 708utilizes trained models 711 to determine a close match for a queryreceived from a knowledge seeker. The deep learning module 709corresponds to the multi-learning layer disclosed in the detaileddescription of FIG. 1 , and uses conversation audits 712 to trainartificial intelligence and machine learning algorithms, and fine-tunesthe algorithms to generate an appropriate response to the query of theknowledge seeker. As disclosed in the detailed description of FIG. 1 , aquery passes through a natural understanding layer, then the patternrecognition module 707, then the NLP module 708, and finally the deeplearning module 709 only when a suitable response to a query is notavailable through a previous module. If the query is answered using thepattern recognition module 707, then the query is not seen by the NLPmodule 708 or the deep learning module 709.

The chatbot platform 403 ensures security 414, as disclosed in thedetailed description of FIG. 4 , for communication with other componentsof the smart-learning and knowledge retrieval system (SLKRS) 201 e,communication with the third party services via the integration module,and communication with the user 701. The chatbot platform 403 also usesthe message bus 413 to communicate with other components, for example,the microservices platform 407, of the SLKRS 201 e. The SLKRS 201 emakes the functionality of the chatbot platform 403 available, forexample, to an enterprise that uses the SLKRS 201 e, to allow users ofthe chatbot platform 403 to incorporate its functionality in theirlegacy software or as part of their offerings to their customers similarto the AIaaS platform 613 disclosed in the detailed description of FIG.6 . The SLKRS 201 e accomplishes the offering of the chatbot platform's403 functionality through a technology platform 713 providing itsfunctionality as a substratum for other applications to build on.

FIG. 8 exemplarily illustrates a software model for the smart-learningand knowledge retrieval system (SLKRS) 201 e. The software modelcomprises a base layer 801, an intermediate layer 802, and an upperlayer 803. The base layer 801 comprises an artificial intelligence andmachine learning platform with corresponding algorithms, machinelearning models, and scores used by the algorithms to make artificiallyintelligent decisions based on queries and feedback received fromknowledge seekers interacting with the SLKRS 201 e. The base layer 801also comprises a learning record store, which is a repository forlearning records of the knowledge seekers using the SLKRS 201 e, and amessaging infrastructure that the SLKRS 201 e uses to interact with theknowledge seekers and to enable interaction among the knowledge seekersand stakeholders of the SLKRS 201 e including an enterprise using theSLKRS 201 e. The intermediate layer 802 provides the ability to engagewith the client 301, manage content and delivery, perform analytics,adapt to the client's requirements and learning capability, and generateknowledge graphs. The intermediate layer 802 further comprises alearning management system (LMS) that the SLKRS 201 e uses to administere-learning courses to knowledge seekers, track their progress, anddocument their progress in the e-learning courses. The intermediatelayer 802 further comprises analytics for assessment of a knowledgeseeker's progress in an e-learning course. The upper layer 803 comprisesthe features that the SLKRS 201 e implements via the base layer 801 andthe intermediate 802 to provide to the knowledge seekers and thestakeholders of the SLKRS 201 e. As exemplarily illustrated, somenon-limiting examples of the features include course content,certifications, payment platforms, social network, curriculum, bots,reports, assessments and tests and important notifications. To elaboratefurther, in one embodiment, the client 301, disclosed in the detaileddescription of FIG. 3 , will be able to access all the details of allthe knowledge seekers' courses including course material, curriculum,and certifications. In another embodiment, the client 301 has access tosocial media platforms related to discussion on the courses amongvarious stakeholders. As an example, the client 301 has access toreports and assessments of the SLKRS 201 e. In another example, thee-learning platform enables knowledge seekers to make payments for theircourses.

FIG. 9 is a block diagram representation of core strategy and drivers orfunctional enablers 901 to ensure optimal user experience for aknowledge seeker seeking knowledge on the smart-learning and knowledgeretrieval system (SLKRS) 201 e. Among the functional enablers or driversof the SLKRS 201 e, the first enabler 902 is being immersive whichimplies interactive and participative features. Such features encourageand include rich user interactions, viral ability, rich visualizations,crowd sourcing questions and involve social media. The second enabler903 is about providing a personalized experience and includes havinghigh touch, customizable, and influential features. Such featurescomprise providing real time responses, having the option of preferencesettings, and a personalization engine. A third enabler 904 provides aseamless user experience and involves being responsive and connected.Such a requirement comprises having adaptive content, a target channel,social sharing capabilities, and community links. Finally, a fourthenabler 905 is being brand driven, which refers to the personality andreliability of the platform. These comprise branding, providinginteractive chatbots and characters, an interaction patterns librarythat captures patterns in interaction between knowledge seekers and theSLKRS 201 e, and using reusable communication templates that areimmediately recognizable as being part of the SLKRS 201 e.

FIG. 10 exemplarily illustrates an architecture for the deployment,scaling, and management of the smart-learning and knowledge retrievalsystem (SLKRS) 201 e. The SLKRS 201 e can be deployed, scaled, andmanaged using a Kubernetes architecture as the SLKRS 201 e usesmicroservices, which lend themselves to containerization andscalability. Kubernetes manages compute, network, and storage resourcesto meet the demand. The Kubernetes master 1001, a set of processes,ensures the resources are available to the microservices of the SLKRS201 e. The Kubernetes master 1001 comprises a scheduler 1001 a,controllers 1001 b, etcd 1001 c, and an application programminginterface (API) server 1001 d. The Kubernetes cluster of machines orvirtual machines comprises nodes 1002, wherein each node 1002 acomprises a Kubelet 1002 b, a kube-proxy 1002 c, and at least one pod1002 d. The Kubernetes master 1001 forms the control plane managing thenodes and their states comprising the microservices they run. A pod 1002d is group of one or more containers, which in turn are standard unitsof software that include program code with all dependencies of theprogram enabling a microservice to run reliably and quickly whereverthey are made to run. Thus, the microservices in containers can run onany node in the cluster. The pod 1002 d shares network and storage amongthe containers and includes a specification of how to run itscontainers. The kubelet 1002 b is a software agent making sure thecontainers are executing in a pod. The kube-proxy 1002 c is a networkproxy that ensures network rules are followed in communication amongnodes and between the nodes and other components of the SLKRS 201 e.

In the Kubernetes master 1001, the scheduler 1001 a allocates nodes topods scheduling the microservices of the microservices platform 407,exemplarily illustrated in FIG. 4 , to run on the machines or thevirtual machines in the cluster. The controllers 1001 b ensures accessthrough lease tokens, for example, to nodes to the microservices andmanages the execution of the microservices on the nodes. The controllers1001 b also ensure fault tolerance by moving the microservices to othernodes when the nodes that they are initially running on fail. Etcd 1001c provides storage for key-value pairs for use of the nodes in thecluster. The API server 1001 d is the front end for the control planerepresented by the Kubernetes master 1001 providing admin access 1003 tothe Kubernetes architecture allowing administration of the architecture.The Kubernetes architecture lends itself to the microservicearchitecture, exemplarily illustrated in FIG. 3 , of the smart-learningand knowledge retrieval system (SLKRS) 201 e due to the containerizationof software as needed by the microservices, deployment of themicroservices to create instances on different servers for faster accessat different geographical locations, and scalability as needed increating instances of microservices to cater to increases in demand forthe microservices. In an embodiment, the Kubernetes architecture is usedwith Docker platform to enable the creation, sharing, and running of themicroservices in containers. The Kubernetes architecture provides anideal method of allocating resources to the microservices that the SLKRS201 e runs and for the features of the microservices that themicroservices platform 407 uses, for example, the services container501, security framework 502, services deployment 503, caching and cachemanagement 505, etc., as disclosed in the detailed description of theinternals of the microservices platform 407 in FIG. 5 .

FIG. 11 exemplarily illustrates an interface that the smart-learning andknowledge retrieval system (SLKRS) 201 e provides to knowledge seekersfor accessing some of its microservices. The interface provides a triallink 1101 b for knowledge seekers to try out a microservice 1101 aoffered by the SLKRS 201 e. The knowledge seekers' access to themicroservice 1101 a can be limited through time bounds, bounds onfeatures of the microservice 1101 a, or on the number of times thefeatures may be used. Through the trial, the SLKRS 201 e allows aknowledge seeker to ascertain if they want to proceed with using theSLKRS 201 e for their e-learning and also give him or her an overview ofall the microservices 1101 a that they can have access to. The interfaceincludes a registration link 1101 c for each microservice 1101 a thatknowledge seekers use to register for using the microservice 1101 a.Through the registration process, a knowledge seeker can opt to use onlythe microservices 1101 a that the knowledge seeker needs, laterexpanding on other microservices 1101 a as needed. The interfaceprovides access to the SLKRS 201 e in the way the microservicearchitecture of the SLKRS 201 e is envisioned—a modularized, scalable,and flexible method of providing individual services that is ideal forproviding adaptive and personalized e-learning with the knowledge seekerin control of his or her e-learning.

FIG. 12 illustrates a graphical user interface (GUI) 201 c of anexemplary embodiment of the smart-learning and knowledge retrievalsystem (SLKRS) 201 e depicting an e-learning course titled “Introductionto computer programming with Scratch”. The GUI 201 c provided by theSLKRS 201 e includes a teacher bot 1201 and conversational help bots1202 for on-demand contextual help 1203. The SLKRS 201 e provides forsophisticated navigation tools 1204, exemplarily illustrated byclickable links for “lessons” and “my courses”. Furthermore, there arenavigation tools provided to switch between a programming tool,indicated as “scratch editor” and a tutorial. The SLKRS 201 e alsoprovides a personalized experience by providing notifications andmessaging 1205. Additionally, there is rich visualization that makes thecourse highly interactive and immersive to enable a dynamic learningenvironment.

The learning path algorithm maximizes the learning potential of thestudent by guiding the student on a learning path. It sets a challenginglearning schedule and keeps the student interested and challengedthrough the learning process. It avoids the pitfall of serving problemsat the same difficulty level to the student.

A lesson has many topics and subtopics, and the system tracks theprogress of the student as they learn the topics and subtopics.

The learning path algorithm will push specific content to the knowledgeseeker based on the type of learner the system perceives the knowledgeseeker to be, such as a fast learner, slow learner, depending on thelearning style of student, defined by the amount of time spent by thestudent on a topic, rate of understanding, frequency of visiting a topicof study, and performance in assessments.

Examples of content presented to the student include videos, documents,code etc. The student can make their notes on the user interface thatdisplays the pushed content by the learning path algorithm. The contentrecommended by the learning path algorithm allows the student to viewthe current courses, lessons and gives the perception of a “live book”.The student's performance in tests is closely monitored, including thetime taken to answer each question. The SLKRS system analyses the numberof questions attempted, number of questions correctly answered, numberof questions incorrectly answered, etc.

FIG. 13 illustrates a graphical user interface of an exemplaryembodiment of a learning path. FIG. 14 illustrates a graphical userinterface of an exemplary embodiment of a learning path with asmoothened path, depicting continuous course setting at each sub topicor topic level.

For a new student, the learning path algorithm sets the initial learninglevel of the student as average or an “average expected level”. Thelearning path algorithm observes and computes learning proficiency ofthe student along the learning path, as represented by dots in FIG. 13 .The learning path algorithm computes general observation points andstate transition points. At the state transition point, the algorithmdetermines the learning path, whether it is on the expected path ordeviates positively up or negatively down from the path. There is aninput function and output function in the learning path algorithm, forexample between lesson 1 and lesson 2, the system observes how thestudent is learning after leaving lesson 1 and entering lesson two.

FIG. 13 represents a first embodiment, wherein the completion of alesson is taken as a point of transition to take the student on a pathupwards (improving performance) or downwards (degrading performance).

In another embodiment, at a fine grained level, the learning pathalgorithm can exercise transition at a level lower than at the discretelevel, i.e., it could exercise transition at any point in time when astudent is learning a specific topic within a lesson. Hence, the graphdepicted in FIG. 14 is a smoothened curve as observations and inferencesare made at a topic level within a lesson at a rapid frequency.

In FIG. 13 , the transitioning occurs when a lesson is exited, and thestudent embarks on the next lesson. Based on all the cumulativeobservations made between lesson 1 and lesson 2, the learning pathalgorithm transitions upwards or downwards. FIG. 13 illustrates that thestudent is being challenged as they learn, and the student is showinglearning progress in that particular learning journey. This progress isdepicted as an upward sloped learning path, for example between lesson 2and 3. The content provided for each learning path is optimized for thatparticular learning path and differs from the content of other learningpaths. For example, the content of lesson 4 under the “Good” categorywill be different from that presented from the content of lesson 4 in“Poor” category.

There are two embodiments for performance assessment applied by thelearning path algorithm. In the first embodiment, the performanceassessment is based on the performance in the current lesson or topic.In the second assessment, the performance assessment is based on anaggregate computation of performance in all previous lessons/topics. Forexample, subjects such as History are amenable to assessment by discreteperformance assessment at the current lesson, whereas, subjects such asMathematics are amenable for aggregate level assessment. For example,poor performance in one particular mathematics problem cannot beinterpreted as a drop in performance in the subject that requires atransitional course correction on the learning path. The statetransitions can be determined at each observation, or can be determinedafter an aggregation of observation points.

The learning path can be provided at a sub topic, topic or lesson level,i.e. can be provided at any level of granularity. The learning pathindicates or predicts the learning capacity of the student underobservation. The learning path maximizes the quantity and complexity ofwhat the student can learn in a given amount of time. For example, if astudent is performing well in Basic Calculus, the next set of problemsand lesson at a higher level of Calculus will be presented to thestudent to challenge them and make good use of their time, instead ofrepeatedly presenting problems at a lower level of difficulty.

The learning path provide a flexible learning option for the studentwherein it can slow down or hasten the learning pace based on student'sindicated preference.

In one embodiment, the learning path algorithm is connected to otheralgorithms, such as a retention algorithm. For example, a student'slearning path may repeat the learning of a particular topic based onretention findings from the retention algorithm.

One of the exemplary methods to determine learning paths is to implementa variation of the Hidden Markov model.

It will be readily apparent in different embodiments that the variousmethods, algorithms, and computer programs disclosed herein areimplemented on non-transitory computer readable storage mediaappropriately programmed for computing devices. The non-transitorycomputer readable storage media participates in providing data, forexample, instructions that are read by a computer, a processor or asimilar device. In different embodiments, the “non-transitory computerreadable storage media” further refers to a single medium or multiplemedia, for example, a centralized database, a distributed database,and/or associated caches and servers that store one or more sets ofinstructions that are read by a computer, a processor or a similardevice. The “non-transitory computer readable storage media” furtherrefers to any medium capable of storing or encoding a set ofinstructions for execution by a computer, a processor or a similardevice and that causes a computer, a processor or a similar device toperform any one or more of the methods disclosed herein. Common forms ofnon-transitory computer readable storage media comprise, for example, afloppy disk, a flexible disk, a hard disk, magnetic tape, a laser disc,a Blu-ray Disc® of the Blu-ray Disc Association, any magnetic medium, acompact disc-read only memory (CD-ROM), a digital versatile disc (DVD),any optical medium, a flash memory card, punch cards, paper tape, anyother physical medium with patterns of holes, a random access memory(RAM), a programmable read only memory (PROM), an erasable programmableread only memory (EPROM), an electrically erasable programmable readonly memory (EEPROM), a flash memory, any other memory chip orcartridge, or any other medium from which a computer can read.

In an embodiment, the computer programs that implement the methods andalgorithms disclosed herein are stored and transmitted using a varietyof media, for example, the computer readable media in a number ofmanners. In an embodiment, hard-wired circuitry or custom hardware isused in place of, or in combination with, software instructions forimplementing the processes of various embodiments. Therefore, theembodiments are not limited to any specific combination of hardware andsoftware. The computer program codes comprising computer executableinstructions can be implemented in any programming language. Examples ofprogramming languages that can be used comprise C, C++, C#, Java®,JavaScript®, Fortran, Ruby, Perl®, Python®, Visual Basic®, hypertextpre-processor (PHP), Microsoft® .NET, Objective-C®, etc. Otherobject-oriented, functional, scripting, and/or logical programminglanguages can also be used. In an embodiment, the computer program codesor software programs are stored on or in one or more mediums as objectcode. In another embodiment, various aspects of the method and thesmart-learning and knowledge retrieval system (SLKRS) 201 e disclosedherein are implemented in a non-programmed environment comprisingdocuments created, for example, in a hypertext markup language (HTML),an extensible markup language (XML), or other format that render aspectsof a graphical user interface (GUI) or perform other functions, whenviewed in a visual area or a window of a browser program. In anotherembodiment, various aspects of the method and the SLKRS 201 e disclosedherein are implemented as programmed elements, or non-programmedelements, or any suitable combination thereof.

Where databases are described such as the database 2010 and the one ormore client databases 201 h, it will be understood by one of ordinaryskill in the art that (i) alternative database structures to thosedescribed may be employed, and (ii) other memory structures besidesdatabases may be employed. Any illustrations or descriptions of anysample databases disclosed herein are illustrative arrangements forstored representations of information. In an embodiment, any number ofother arrangements are employed besides those suggested by tablesillustrated in the drawings or elsewhere. Similarly, any illustratedentries of the databases represent exemplary information only; one ofordinary skill in the art will understand that the number and content ofthe entries can be different from those disclosed herein. In anotherembodiment, despite any depiction of the databases as tables, otherformats including relational databases, object-based models, and/ordistributed databases are used to store and manipulate the data typesdisclosed herein. Object methods or behaviors of a database can be usedto implement various processes such as those disclosed herein. Inanother embodiment, the databases are, in a known manner, stored locallyor remotely from a device that accesses data in such a database. Inembodiments where there are multiple databases in the smart-learning andknowledge retrieval system (SLKRS) 201 e, the databases are integratedto communicate with each other for enabling simultaneous updates of datalinked across the databases, when there are any updates to the data inone of the databases.

The method and the smart-learning and knowledge retrieval system (SLKRS)201 e disclosed herein can be configured to work in a networkenvironment comprising one or more computers that are in communicationwith one or more devices via a network. In an embodiment, the computerscommunicate with the devices directly or indirectly, via a wired mediumor a wireless medium such as the Internet, a local area network (LAN), awide area network (WAN) or the Ethernet, a token ring, or via anyappropriate communications mediums or combination of communicationsmediums. Each of the devices comprises processors, examples of which aredisclosed above, that are adapted to communicate with the computers. Inan embodiment, each of the computers is equipped with a networkcommunication device, for example, a network interface card, a modem, orother network connection device suitable for connecting to a network.Each of the computers and the devices executes an operating system,examples of which are disclosed above. While the operating system maydiffer depending on the type of computer, the operating system providesthe appropriate communications protocols to establish communicationlinks with the network. Any number and type of machines may be incommunication with the computers.

The method and the smart-learning and knowledge retrieval system (SLKRS)201 e disclosed herein are not limited to a particular computer systemplatform, processor, operating system, or network. In an embodiment, oneor more aspects of the method and the SLKRS 201 e disclosed herein aredistributed among one or more computer systems, for example, serversconfigured to provide one or more services to one or more clientcomputers, or to perform a complete task in a distributed system. Forexample, one or more aspects of the method and the SLKRS 201 e disclosedherein are performed on a client-server system that comprises componentsdistributed among one or more server systems that perform multiplefunctions according to various embodiments. These components comprise,for example, executable, intermediate, or interpreted code, whichcommunicate over a network using a communication protocol. The methodand the SLKRS 201 e disclosed herein are not limited to be executable onany particular system or group of systems, and are not limited to anyparticular distributed architecture, network, or communication protocol.

The foregoing examples have been provided merely for explanation and arein no way to be construed as limiting of the smart-learning andknowledge retrieval system (SLKRS) 201 e disclosed herein. While theSLKRS 201 e has been described with reference to various embodiments, itis understood that the words, which have been used herein, are words ofdescription and illustration, rather than words of limitation.Furthermore, although the SLKRS 201 e has been described herein withreference to particular means, materials, and embodiments, the SLKRS 201e is not intended to be limited to the particulars disclosed herein;rather, the SLKRS 201 e extends to all functionally equivalentstructures, methods and uses, such as are within the scope of theappended claims. While multiple embodiments are disclosed, it will beunderstood by those skilled in the art, having the benefit of theteachings of this specification, that the SLKRS 201 e disclosed hereinare capable of modifications and other embodiments may be effected andchanges may be made thereto, without departing from the scope and spiritof the SLKRS 201 e disclosed herein.

We claim:
 1. A computer-implemented method for providing adaptive andpersonalized e-learning based on continually, artificially learnedunique characteristics of a knowledge seeker, the method employing asmart-learning and knowledge retrieval system executable by at least oneprocessor configured to execute computer program instructions forperforming the method, the method comprising: ingesting data from aplurality of sources in a plurality of formats by the smart-learning andknowledge retrieval system; merging the ingested data into a knowledgebase to create an ontology by the smart-learning and knowledge retrievalsystem, wherein the ingested data is merged into the knowledge basebased on computed strengths of terms found in the plurality of sourcesfrom which the data is ingested; assimilating the merged data togenerate experiences from the assimilated data by the smart-learning andknowledge retrieval system; receiving a query from the knowledge seekerthrough one of a plurality of interfaces by the smart-learning andknowledge retrieval system; retrieving one of a generated experience andan experience created based on an artificially intelligent understandingof the received query by the smart-learning and knowledge retrievalsystem; sending the retrieved experience to the knowledge seeker in animmersive format via one or more of the plurality of interfaces by thesmart-learning and knowledge retrieval system for the knowledge seekerto interact with the sent experience; receiving feedback from theknowledge seeker via the one of the plurality of interfaces by thesmart-learning and knowledge retrieval system in response to the sentexperience; computing a score for the knowledge seeker continually basedon each of the received query and the received feedback by thesmart-learning and knowledge retrieval system, thereby artificiallylearning unique characteristics of the knowledge seeker for measuring anability of the knowledge seeker to learn and to show continued interestin an e-learning course; and generating a learning path for theknowledge seeker as a graphical output on a user interface, wherein aplurality of state transition points of the learning path define thelearning path, and wherein the learning path is determined byperformance of the knowledge seeker over one or more of sub topics,topics, and lessons.
 2. The computer implemented method of claim 1,wherein a learning path algorithm observes and computes learningproficiency of the knowledge seeker along the learning path, andcomputes general observation points and said state transition points. 3.The computer-implemented method of claim 2, wherein said learning pathalgorithm pushes specific content to the knowledge seeker based on typeof learning exhibited by the knowledge seeker, such as fast learning,slow learning, learning style, frequency of visiting a topic of study,and performance in assessments.
 4. The computer-implemented method ofclaim 2, wherein the learning path algorithm sets an initial learninglevel of a new knowledge seeker to a level indicative of an averagelevel or an average expected level in said learning path.
 5. Thecomputer implemented method of claim 4, wherein completion of eachlesson is considered as a corresponding state transition point of theplurality of state transition points to take the knowledge seeker on aprojected path upwards when the knowledge seeker exhibits improvingperformance or on a path downwards when the knowledge seeker exhibitsdeteriorating performance.
 6. The computer implemented method of claim5, wherein a projected learning path after the corresponding statetransition point is determined based on the performance of the knowledgeseeker in a current lesson or topic.
 7. The computer implemented methodof claim 5, wherein the projected path after the corresponding statetransition point is determined based on aggregated computed performanceof the knowledge seeker in all previous lessons and topics leading to acurrent lesson or topic.
 8. The computer implemented method of claim 2,wherein the learning path algorithm takes inputs from a retentionalgorithm to determine the learning path.
 9. The computer implementedmethod of claim 2, wherein the learning path algorithm takes inputs froma knowledge concept graph to determine the learning path.
 10. Thecomputer implemented method of claim 2, wherein the learning pathalgorithm applies a modified Hidden Markov model.
 11. The computerimplemented method of claim 1, wherein microservices predict performanceof the knowledge seeker to provide an insight into a projected learningpath should they proceed in the same manner as they are proceeding at acurrent time.
 12. The computer implemented method of claim 1, whereinperformance in regular assessments of knowledge acquired by theknowledge seeker, and direct feedback from the knowledge seeker are usedas inputs to derive the state transition points of the learning path.13. An electronic device employing a smart-learning and knowledgeretrieval system for providing adaptive and personalized e-learningbased on continually artificially learned unique characteristics of aknowledge seeker, the electronic device comprising: a non-transitorycomputer readable storage medium configured to store computer programinstructions defined by the smart-learning and knowledge retrievalsystem; at least one processor communicatively coupled to thenon-transitory computer readable storage medium, the at least oneprocessor configured to execute the defined computer programinstructions; a display screen configured to display a graphical userinterface provided by the smart-learning and knowledge retrieval system;and the smart-learning and knowledge retrieval system comprising: aningestion module configured to ingest data from a plurality of sourcesin a plurality of formats; a merge module configured to merge theingested data into a knowledge base to create an ontology, wherein theingested data is merged into the knowledge base based on strengths ofterms found in the plurality of sources from which the data is ingested,and wherein the strengths of the terms are computed by a computationmodule; an assimilation module configured to assimilate the merged datato generate experiences from the assimilated data; a data transfermodule configured to receive a query from a knowledge seeker through oneof a plurality of interfaces via the graphical user interface on theelectronic device; a retrieval module configured to retrieve one of agenerated experience and an experience created based on an artificiallyintelligent understanding of the received query; the data transfermodule further configured to send the retrieved experience to theknowledge seeker in an immersive format via one or more of the pluralityof interfaces for the knowledge seeker to interact with the sentexperience; the data transfer module further configured to receivefeedback from the knowledge seeker via the one of the plurality ofinterfaces in response to the sent experience; the computation modulefurther configured to compute a score for the knowledge seekercontinually based on each of the received query and the receivedfeedback, thereby artificially learning unique characteristics of theknowledge seeker for measuring an ability of the knowledge seeker tolearn and to show continued interest in an e-learning course; and alearning path module that generates a learning path for the knowledgeseeker as a graphical output on a user interface, wherein a plurality ofstate transition points of the learning path define the learning path,and wherein the learning path is determined by performance of theknowledge seeker over one or more of sub topics, topics, or lessons. 14.A non-transitory computer readable storage medium having embodiedthereon, computer program codes comprising instructions executable by atleast one processor for providing adaptive and personalized e-learningbased on continually artificially learned unique characteristics of aknowledge seeker, the computer program codes comprising: a firstcomputer program code for ingesting data from a plurality of sources ina plurality of formats; a second computer program code for merging theingested data into a knowledge base to create an ontology, wherein theingested data is merged into the knowledge base based on computedstrengths of terms found in the plurality of sources from which the datais ingested; a third computer program code for assimilating the mergeddata to generate experiences from the assimilated data; a fourthcomputer program code for receiving a query from the knowledge seekerthrough one of a plurality of interfaces; a fifth computer program codefor retrieving one of a generated experience and an experience createdbased on an artificially intelligent understanding of the receivedquery; a sixth computer program code for sending the retrievedexperience to the knowledge seeker in an immersive format via one ormore of the plurality of interfaces for the knowledge seeker to interactwith the sent experience; a seventh computer program code for receivingfeedback from the knowledge seeker via the one of the plurality ofinterfaces in response to the sent experience; an eighth computerprogram code for computing a score for the knowledge seeker continuallybased on each of the received query and the received feedback, therebyartificially learning unique characteristics of the knowledge seeker formeasuring an ability of the knowledge seeker to learn and to showcontinued interest in an e-learning course; and a ninth computer programcode for generating a learning path for the knowledge seeker as agraphical output on a user interface, wherein a plurality of statetransition points of the learning path define the learning path, andwherein the learning path is determined by performance of the knowledgeseeker over one or more of sub topics, topics, and lessons.
 15. Thenon-transitory computer readable storage medium of claim 14, wherein alearning path algorithm observes and computes learning proficiency ofthe knowledge seeker along the learning path, and computes generalobservation points and said state transition points.
 16. Thenon-transitory computer readable storage medium of claim 15, whereinsaid learning path algorithm pushes specific content to the knowledgeseeker based on type of learning exhibited by the knowledge seeker, suchas fast learning, slow learning, learning style, frequency of visiting atopic of study, and performance in assessments.
 17. The non-transitorycomputer readable storage medium of claim 15, wherein the learning pathalgorithm sets an initial learning level of a new knowledge seeker to alevel indicative of an average level or an average expected level insaid learning path.
 18. The non-transitory computer readable storagemedium of claim 17, wherein completion of each lesson is considered as acorresponding state transition point of the plurality of statetransition points to take the knowledge seeker on a projected pathupwards when the knowledge seeker exhibits improving performance or on apath downwards when the knowledge seeker exhibits deterioratingperformance.
 19. The non-transitory computer readable storage medium ofclaim 18, wherein a projected learning path after the correspondingstate transition point is determined based on the performance of theknowledge seeker in a current lesson or topic.
 20. The non-transitorycomputer readable storage medium of claim 18, wherein the projected pathafter the corresponding state transition point is determined based onaggregated computed performance of the knowledge seeker in all previouslessons and topics leading to a current lesson or topic.